This post includes a comprehensive summary of the process we went through to build a machine learning (ML) model for the Data Science Prediction Event created under the framework of the ISFOG2020 conference, to be held in Texas (August 16-19, 2020). The competition was hosted in Kaggle and run through a period of eight months, from April to December 2019. The problem proposed for the prediction event is to use piezocone (CPTu, herein and after denoted as CPT) data to predict blowcounts needed for the installation of jacket piles, located in North Sea soil.
We start with loading the data and by doing some exploratory data analysis (EDA), trying to get some initial insights. Then we explore possibilities to incorporate engineering/geotechnical knowledge by introducing new parameters in the analysis. Next, we dive into the model-building process, where we train various ML models, moving from Simple Linear Regressions to more complex models like Neural Networks.
All the analysis is performed using R and the RStudio environment. This report is generated using R Markdown. You may use the content panel on the top-left of the screen to easily navigate through the document. The Hide/Code buttons on the top-right make it possible to hide/show the R code used to perform the analysis.
We have peviously downloaded the data from here and saved them locally. We set the working directory so all the work is saved in one location. Various data sets were provided so we are going to separately load and comment on them.
The available data contain information about the soil conditions and installation of 114 piles. These data are divided into two groups:
training - which contains information on 94 piles;validation - which contains information on 20 piles.The training data will be used to train the ML model while the validation data will be used to validate the model and assess it’s performance. Let’s read and view the data.
training <- read_csv("F:/isfog2020/training_data_cleaned.csv")
validation <- read_csv("F:/isfog2020/validation_data_cleaned.csv")kable(top_n(training, 100),
digits = 3,
caption = "Training data.",
align = "r") %>%
kable_styling(font_size = 11) %>%
row_spec(0, color = "grey10", background = "#F4F5F6") %>%
scroll_box(height = "300px")| z [m] | qc [MPa] | fs [MPa] | u2 [MPa] | ID | Location ID | Blowcount [Blows/m] | Normalised ENTRHU [-] | Normalised hammer energy [-] | Number of blows | Diameter [m] | Bottom wall thickness [mm] | Pile penetration [m] |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4.0 | 11.434 | 0.082 | 0.036 | AA__4_0 | AA | 8 | 0.130 | 0.173 | 8.00 | 2.48 | 55 | 34 |
| 4.5 | 14.955 | 0.109 | 0.035 | AA__4_5 | AA | 8 | 0.140 | 0.187 | 11.00 | 2.48 | 55 | 34 |
| 5.0 | 15.722 | 0.125 | 0.037 | AA__5_0 | AA | 8 | 0.151 | 0.201 | 14.00 | 2.48 | 55 | 34 |
| 5.5 | 15.727 | 0.123 | 0.039 | AA__5_5 | AA | 16 | 0.131 | 0.175 | 29.00 | 2.48 | 55 | 34 |
| 6.0 | 11.347 | 0.111 | 0.045 | AA__6_0 | AA | 24 | 0.112 | 0.149 | 44.00 | 2.48 | 55 | 34 |
| 6.5 | 13.989 | 0.105 | 0.051 | AA__6_5 | AA | 22 | 0.147 | 0.195 | 55.00 | 2.48 | 55 | 34 |
| 7.0 | 16.632 | 0.098 | 0.058 | AA__7_0 | AA | 20 | 0.182 | 0.242 | 66.00 | 2.48 | 55 | 34 |
| 7.5 | 24.536 | 0.146 | 0.063 | AA__7_5 | AA | 24 | 0.174 | 0.233 | 77.50 | 2.48 | 55 | 34 |
| 8.0 | 24.622 | 0.183 | 0.059 | AA__8_0 | AA | 28 | 0.167 | 0.223 | 89.00 | 2.48 | 55 | 34 |
| 8.5 | 24.245 | 0.191 | 0.058 | AA__8_5 | AA | 34 | 0.171 | 0.228 | 105.50 | 2.48 | 55 | 34 |
| 9.0 | 28.871 | 0.175 | 0.058 | AA__9_0 | AA | 40 | 0.175 | 0.233 | 122.00 | 2.48 | 55 | 34 |
| 9.5 | 25.603 | 0.181 | 0.061 | AA__9_5 | AA | 64 | 0.181 | 0.242 | 162.00 | 2.48 | 55 | 34 |
| 10.0 | 28.036 | 0.153 | 0.069 | AA__10_0 | AA | 88 | 0.188 | 0.251 | 202.00 | 2.48 | 55 | 34 |
| 10.5 | 25.148 | 0.132 | 0.089 | AA__10_5 | AA | 88 | 0.206 | 0.274 | 245.50 | 2.48 | 55 | 34 |
| 11.0 | 24.442 | 0.203 | 0.087 | AA__11_0 | AA | 88 | 0.223 | 0.298 | 289.00 | 2.48 | 55 | 34 |
| 11.5 | 6.894 | 0.084 | 0.085 | AA__11_5 | AA | 74 | 0.225 | 0.300 | 325.50 | 2.48 | 55 | 34 |
| 12.0 | 5.528 | 0.039 | 0.020 | AA__12_0 | AA | 60 | 0.227 | 0.302 | 362.00 | 2.48 | 55 | 34 |
| 12.5 | 6.805 | 0.052 | 0.097 | AA__12_5 | AA | 36 | 0.251 | 0.334 | 372.00 | 2.48 | 55 | 34 |
| 13.0 | 6.492 | 0.048 | 0.106 | AA__13_0 | AA | 12 | 0.275 | 0.367 | 382.00 | 2.48 | 55 | 34 |
| 13.5 | 8.541 | 0.032 | -0.043 | AA__13_5 | AA | 10 | 0.269 | 0.358 | 387.50 | 2.48 | 55 | 34 |
| 14.0 | 28.151 | 0.167 | -0.114 | AA__14_0 | AA | 8 | 0.262 | 0.350 | 393.00 | 2.48 | 55 | 34 |
| 14.5 | 23.779 | 0.187 | -0.067 | AA__14_5 | AA | 10 | 0.214 | 0.286 | 398.50 | 2.48 | 55 | 34 |
| 15.0 | 12.522 | 0.118 | -0.362 | AA__15_0 | AA | 12 | 0.166 | 0.221 | 404.00 | 2.48 | 55 | 34 |
| 15.5 | 6.764 | 0.043 | -0.339 | AA__15_5 | AA | 20 | 0.187 | 0.249 | 414.50 | 2.48 | 55 | 34 |
| 16.0 | 7.552 | 0.052 | -0.261 | AA__16_0 | AA | 28 | 0.208 | 0.277 | 425.00 | 2.48 | 55 | 34 |
| 16.5 | 5.400 | 0.022 | -0.145 | AA__16_5 | AA | 22 | 0.213 | 0.284 | 432.50 | 2.48 | 55 | 34 |
| 17.0 | 5.900 | 0.017 | -0.093 | AA__17_0 | AA | 16 | 0.219 | 0.292 | 440.00 | 2.48 | 55 | 34 |
| 17.5 | 4.048 | 0.024 | -0.144 | AA__17_5 | AA | 28 | 0.213 | 0.284 | 450.50 | 2.48 | 55 | 34 |
| 18.0 | 20.823 | 0.108 | -0.294 | AA__18_0 | AA | 40 | 0.207 | 0.276 | 461.00 | 2.48 | 55 | 34 |
| 18.5 | 27.700 | 0.225 | -0.238 | AA__18_5 | AA | 70 | 0.214 | 0.286 | 507.00 | 2.48 | 55 | 34 |
| 19.0 | 25.179 | 0.177 | -0.188 | AA__19_0 | AA | 100 | 0.222 | 0.296 | 553.00 | 2.48 | 55 | 34 |
| 19.5 | 22.926 | 0.151 | -0.069 | AA__19_5 | AA | 108 | 0.255 | 0.341 | 611.00 | 2.48 | 55 | 34 |
| 20.0 | 20.674 | 0.124 | 0.049 | AA__20_0 | AA | 116 | 0.289 | 0.386 | 669.00 | 2.48 | 55 | 34 |
| 20.5 | 25.997 | 0.231 | 0.185 | AA__20_5 | AA | 126 | 0.293 | 0.390 | 733.00 | 2.48 | 55 | 34 |
| 21.0 | 29.572 | 0.304 | 0.175 | AA__21_0 | AA | 136 | 0.296 | 0.395 | 797.00 | 2.48 | 55 | 34 |
| 21.5 | 40.627 | 0.408 | 0.158 | AA__21_5 | AA | 128 | 0.311 | 0.415 | 859.50 | 2.48 | 55 | 34 |
| 22.0 | 39.834 | 0.368 | 0.170 | AA__22_0 | AA | 120 | 0.326 | 0.434 | 922.00 | 2.48 | 55 | 34 |
| 22.5 | 34.681 | 0.356 | 0.163 | AA__22_5 | AA | 122 | 0.324 | 0.432 | 988.50 | 2.48 | 55 | 34 |
| 23.0 | 26.795 | 0.273 | 0.166 | AA__23_0 | AA | 124 | 0.322 | 0.430 | 1055.00 | 2.48 | 55 | 34 |
| 23.5 | 21.606 | 0.222 | 0.205 | AA__23_5 | AA | 124 | 0.352 | 0.469 | 1118.50 | 2.48 | 55 | 34 |
| 24.0 | 27.394 | 0.295 | 0.197 | AA__24_0 | AA | 124 | 0.381 | 0.508 | 1182.00 | 2.48 | 55 | 34 |
| 24.5 | 20.538 | 0.258 | 0.198 | AA__24_5 | AA | 122 | 0.388 | 0.517 | 1239.50 | 2.48 | 55 | 34 |
| 25.0 | 27.088 | 0.211 | -0.038 | AA__25_0 | AA | 120 | 0.395 | 0.526 | 1297.00 | 2.48 | 55 | 34 |
| 25.5 | 35.413 | 0.319 | 0.210 | AA__25_5 | AA | 104 | 0.426 | 0.568 | 1354.50 | 2.48 | 55 | 34 |
| 26.0 | 36.892 | 0.315 | 0.215 | AA__26_0 | AA | 88 | 0.457 | 0.609 | 1412.00 | 2.48 | 55 | 34 |
| 26.5 | 46.709 | 0.456 | 0.250 | AA__26_5 | AA | 94 | 0.489 | 0.653 | 1459.50 | 2.48 | 55 | 34 |
| 27.0 | 45.741 | 0.473 | 0.238 | AA__27_0 | AA | 100 | 0.522 | 0.696 | 1507.00 | 2.48 | 55 | 34 |
| 27.5 | 50.310 | 0.469 | 0.211 | AA__27_5 | AA | 92 | 0.573 | 0.763 | 1553.00 | 2.48 | 55 | 34 |
| 28.0 | 42.441 | 0.388 | 0.267 | AA__28_0 | AA | 84 | 0.623 | 0.831 | 1599.00 | 2.48 | 55 | 34 |
| 28.5 | 42.527 | 0.487 | 0.292 | AA__28_5 | AA | 84 | 0.618 | 0.824 | 1643.00 | 2.48 | 55 | 34 |
| 29.0 | 53.361 | 0.661 | 0.297 | AA__29_0 | AA | 84 | 0.613 | 0.817 | 1687.00 | 2.48 | 55 | 34 |
| 29.5 | 47.392 | 0.373 | 0.302 | AA__29_5 | AA | 88 | 0.622 | 0.830 | 1731.50 | 2.48 | 55 | 34 |
| 30.0 | 44.926 | 0.521 | 0.308 | AA__30_0 | AA | 92 | 0.632 | 0.843 | 1776.00 | 2.48 | 55 | 34 |
| 30.5 | 46.653 | 0.574 | 0.313 | AA__30_5 | AA | 92 | 0.630 | 0.840 | 1820.00 | 2.48 | 55 | 34 |
| 31.0 | 51.331 | 0.652 | 0.318 | AA__31_0 | AA | 92 | 0.628 | 0.837 | 1864.00 | 2.48 | 55 | 34 |
| 31.5 | 48.796 | 0.531 | 0.323 | AA__31_5 | AA | 90 | 0.628 | 0.837 | 1909.00 | 2.48 | 55 | 34 |
| 32.0 | 46.261 | 0.411 | 0.328 | AA__32_0 | AA | 88 | 0.628 | 0.837 | 1954.00 | 2.48 | 55 | 34 |
| 32.5 | 59.915 | 0.812 | 0.333 | AA__32_5 | AA | 90 | 0.630 | 0.841 | 2001.00 | 2.48 | 55 | 34 |
| 33.0 | 60.356 | 0.810 | 0.338 | AA__33_0 | AA | 92 | 0.633 | 0.844 | 2048.00 | 2.48 | 55 | 34 |
| 33.5 | 57.943 | 0.757 | 0.343 | AA__33_5 | AA | 90 | 0.649 | 0.865 | 2094.00 | 2.48 | 55 | 34 |
| 34.0 | 58.148 | 0.677 | 0.348 | AA__34_0 | AA | 88 | 0.664 | 0.886 | 2140.00 | 2.48 | 55 | 34 |
| 5.0 | 15.722 | 0.125 | 0.037 | AB__5_0 | AB | 8 | 0.118 | 0.157 | 14.00 | 2.48 | 55 | 34 |
| 5.5 | 15.727 | 0.123 | 0.039 | AB__5_5 | AB | 8 | 0.115 | 0.154 | 20.50 | 2.48 | 55 | 34 |
| 6.0 | 11.347 | 0.111 | 0.045 | AB__6_0 | AB | 8 | 0.113 | 0.151 | 27.00 | 2.48 | 55 | 34 |
| 6.5 | 13.989 | 0.105 | 0.051 | AB__6_5 | AB | 6 | 0.151 | 0.202 | 30.00 | 2.48 | 55 | 34 |
| 7.0 | 16.632 | 0.098 | 0.058 | AB__7_0 | AB | 4 | 0.189 | 0.252 | 33.00 | 2.48 | 55 | 34 |
| 7.5 | 24.536 | 0.146 | 0.063 | AB__7_5 | AB | 16 | 0.152 | 0.202 | 43.00 | 2.48 | 55 | 34 |
| 8.0 | 24.622 | 0.183 | 0.059 | AB__8_0 | AB | 28 | 0.114 | 0.152 | 53.00 | 2.48 | 55 | 34 |
| 8.5 | 24.245 | 0.191 | 0.058 | AB__8_5 | AB | 66 | 0.113 | 0.150 | 98.50 | 2.48 | 55 | 34 |
| 9.0 | 28.871 | 0.175 | 0.058 | AB__9_0 | AB | 104 | 0.112 | 0.149 | 144.00 | 2.48 | 55 | 34 |
| 9.5 | 25.603 | 0.181 | 0.061 | AB__9_5 | AB | 90 | 0.137 | 0.182 | 182.00 | 2.48 | 55 | 34 |
| 10.0 | 28.036 | 0.153 | 0.069 | AB__10_0 | AB | 76 | 0.162 | 0.216 | 220.00 | 2.48 | 55 | 34 |
| 10.5 | 25.148 | 0.132 | 0.089 | AB__10_5 | AB | 96 | 0.165 | 0.220 | 266.50 | 2.48 | 55 | 34 |
| 11.0 | 24.442 | 0.203 | 0.087 | AB__11_0 | AB | 116 | 0.168 | 0.224 | 313.00 | 2.48 | 55 | 34 |
| 11.5 | 6.894 | 0.084 | 0.085 | AB__11_5 | AB | 98 | 0.205 | 0.273 | 362.50 | 2.48 | 55 | 34 |
| 12.0 | 5.528 | 0.039 | 0.020 | AB__12_0 | AB | 80 | 0.242 | 0.322 | 412.00 | 2.48 | 55 | 34 |
| 12.5 | 6.805 | 0.052 | 0.097 | AB__12_5 | AB | 68 | 0.244 | 0.325 | 448.50 | 2.48 | 55 | 34 |
| 13.0 | 6.492 | 0.048 | 0.106 | AB__13_0 | AB | 56 | 0.246 | 0.328 | 485.00 | 2.48 | 55 | 34 |
| 13.5 | 8.541 | 0.032 | -0.043 | AB__13_5 | AB | 38 | 0.242 | 0.322 | 495.00 | 2.48 | 55 | 34 |
| 14.0 | 28.151 | 0.167 | -0.114 | AB__14_0 | AB | 20 | 0.237 | 0.316 | 505.00 | 2.48 | 55 | 34 |
| 14.5 | 23.779 | 0.187 | -0.067 | AB__14_5 | AB | 20 | 0.208 | 0.278 | 515.50 | 2.48 | 55 | 34 |
| 15.0 | 12.522 | 0.118 | -0.362 | AB__15_0 | AB | 20 | 0.180 | 0.240 | 526.00 | 2.48 | 55 | 34 |
| 15.5 | 6.764 | 0.043 | -0.339 | AB__15_5 | AB | 24 | 0.188 | 0.250 | 546.50 | 2.48 | 55 | 34 |
| 16.0 | 7.552 | 0.052 | -0.261 | AB__16_0 | AB | 28 | 0.195 | 0.260 | 567.00 | 2.48 | 55 | 34 |
| 16.5 | 5.400 | 0.022 | -0.145 | AB__16_5 | AB | 28 | 0.198 | 0.264 | 581.00 | 2.48 | 55 | 34 |
| 17.0 | 5.900 | 0.017 | -0.093 | AB__17_0 | AB | 28 | 0.201 | 0.268 | 595.00 | 2.48 | 55 | 34 |
| 17.5 | 4.048 | 0.024 | -0.144 | AB__17_5 | AB | 48 | 0.196 | 0.261 | 615.50 | 2.48 | 55 | 34 |
| 18.0 | 20.823 | 0.108 | -0.294 | AB__18_0 | AB | 68 | 0.191 | 0.255 | 636.00 | 2.48 | 55 | 34 |
| 18.5 | 27.700 | 0.225 | -0.238 | AB__18_5 | AB | 82 | 0.225 | 0.299 | 684.50 | 2.48 | 55 | 34 |
| 19.0 | 25.179 | 0.177 | -0.188 | AB__19_0 | AB | 96 | 0.258 | 0.344 | 733.00 | 2.48 | 55 | 34 |
| 19.5 | 22.926 | 0.151 | -0.069 | AB__19_5 | AB | 90 | 0.313 | 0.418 | 779.00 | 2.48 | 55 | 34 |
| 20.0 | 20.674 | 0.124 | 0.049 | AB__20_0 | AB | 84 | 0.369 | 0.492 | 825.00 | 2.48 | 55 | 34 |
| 20.5 | 25.997 | 0.231 | 0.185 | AB__20_5 | AB | 92 | 0.383 | 0.510 | 871.00 | 2.48 | 55 | 34 |
| 21.0 | 29.572 | 0.304 | 0.175 | AB__21_0 | AB | 100 | 0.397 | 0.529 | 917.00 | 2.48 | 55 | 34 |
| 21.5 | 40.627 | 0.408 | 0.158 | AB__21_5 | AB | 104 | 0.385 | 0.513 | 968.00 | 2.48 | 55 | 34 |
| 22.0 | 39.834 | 0.368 | 0.170 | AB__22_0 | AB | 108 | 0.373 | 0.498 | 1019.00 | 2.48 | 55 | 34 |
| 22.5 | 34.681 | 0.356 | 0.163 | AB__22_5 | AB | 102 | 0.419 | 0.559 | 1067.00 | 2.48 | 55 | 34 |
| 23.0 | 26.795 | 0.273 | 0.166 | AB__23_0 | AB | 96 | 0.465 | 0.620 | 1115.00 | 2.48 | 55 | 34 |
| 23.5 | 21.606 | 0.222 | 0.205 | AB__23_5 | AB | 90 | 0.495 | 0.661 | 1159.50 | 2.48 | 55 | 34 |
| 24.0 | 27.394 | 0.295 | 0.197 | AB__24_0 | AB | 84 | 0.526 | 0.702 | 1204.00 | 2.48 | 55 | 34 |
| 24.5 | 20.538 | 0.258 | 0.198 | AB__24_5 | AB | 86 | 0.553 | 0.738 | 1248.50 | 2.48 | 55 | 34 |
| 25.0 | 27.088 | 0.211 | -0.038 | AB__25_0 | AB | 88 | 0.580 | 0.774 | 1293.00 | 2.48 | 55 | 34 |
| 25.5 | 35.413 | 0.319 | 0.210 | AB__25_5 | AB | 86 | 0.590 | 0.787 | 1336.50 | 2.48 | 55 | 34 |
| 26.0 | 36.892 | 0.315 | 0.215 | AB__26_0 | AB | 84 | 0.600 | 0.800 | 1380.00 | 2.48 | 55 | 34 |
| 26.5 | 46.709 | 0.456 | 0.250 | AB__26_5 | AB | 84 | 0.594 | 0.792 | 1432.00 | 2.48 | 55 | 34 |
| 27.0 | 45.741 | 0.473 | 0.238 | AB__27_0 | AB | 84 | 0.589 | 0.786 | 1484.00 | 2.48 | 55 | 34 |
| 27.5 | 50.310 | 0.469 | 0.211 | AB__27_5 | AB | 84 | 0.601 | 0.801 | 1528.00 | 2.48 | 55 | 34 |
| 28.0 | 42.441 | 0.388 | 0.267 | AB__28_0 | AB | 84 | 0.613 | 0.817 | 1572.00 | 2.48 | 55 | 34 |
| 28.5 | 42.527 | 0.487 | 0.292 | AB__28_5 | AB | 86 | 0.610 | 0.813 | 1618.00 | 2.48 | 55 | 34 |
| 29.0 | 53.361 | 0.661 | 0.297 | AB__29_0 | AB | 88 | 0.606 | 0.808 | 1664.00 | 2.48 | 55 | 34 |
| 29.5 | 47.392 | 0.373 | 0.302 | AB__29_5 | AB | 92 | 0.606 | 0.808 | 1710.50 | 2.48 | 55 | 34 |
| 30.0 | 44.926 | 0.521 | 0.308 | AB__30_0 | AB | 96 | 0.607 | 0.809 | 1757.00 | 2.48 | 55 | 34 |
| 30.5 | 46.653 | 0.574 | 0.313 | AB__30_5 | AB | 96 | 0.615 | 0.820 | 1803.50 | 2.48 | 55 | 34 |
| 31.0 | 51.331 | 0.652 | 0.318 | AB__31_0 | AB | 96 | 0.623 | 0.830 | 1850.00 | 2.48 | 55 | 34 |
| 31.5 | 48.796 | 0.531 | 0.323 | AB__31_5 | AB | 96 | 0.617 | 0.822 | 1898.50 | 2.48 | 55 | 34 |
| 32.0 | 46.261 | 0.411 | 0.328 | AB__32_0 | AB | 96 | 0.610 | 0.814 | 1947.00 | 2.48 | 55 | 34 |
| 32.5 | 59.915 | 0.812 | 0.333 | AB__32_5 | AB | 102 | 0.600 | 0.800 | 2000.50 | 2.48 | 55 | 34 |
| 33.0 | 60.356 | 0.810 | 0.338 | AB__33_0 | AB | 108 | 0.590 | 0.786 | 2054.00 | 2.48 | 55 | 34 |
| 33.5 | 57.943 | 0.757 | 0.343 | AB__33_5 | AB | 106 | 0.573 | 0.764 | 2112.50 | 2.48 | 55 | 34 |
| 34.0 | 58.148 | 0.677 | 0.348 | AB__34_0 | AB | 104 | 0.556 | 0.742 | 2171.00 | 2.48 | 55 | 34 |
| 4.0 | 11.434 | 0.082 | 0.036 | AC__4_0 | AC | 6 | 0.118 | 0.158 | 6.50 | 2.48 | 55 | 34 |
| 4.5 | 14.955 | 0.109 | 0.035 | AC__4_5 | AC | 5 | 0.112 | 0.150 | 7.25 | 2.48 | 55 | 34 |
| 5.0 | 15.722 | 0.125 | 0.037 | AC__5_0 | AC | 4 | 0.106 | 0.142 | 8.00 | 2.48 | 55 | 34 |
| 5.5 | 15.727 | 0.123 | 0.039 | AC__5_5 | AC | 10 | 0.104 | 0.139 | 14.50 | 2.48 | 55 | 34 |
| 6.0 | 11.347 | 0.111 | 0.045 | AC__6_0 | AC | 16 | 0.103 | 0.137 | 21.00 | 2.48 | 55 | 34 |
| 6.5 | 13.989 | 0.105 | 0.051 | AC__6_5 | AC | 20 | 0.104 | 0.139 | 31.00 | 2.48 | 55 | 34 |
| 7.0 | 16.632 | 0.098 | 0.058 | AC__7_0 | AC | 24 | 0.106 | 0.142 | 41.00 | 2.48 | 55 | 34 |
| 7.5 | 24.536 | 0.146 | 0.063 | AC__7_5 | AC | 38 | 0.122 | 0.163 | 63.00 | 2.48 | 55 | 34 |
| 8.0 | 24.622 | 0.183 | 0.059 | AC__8_0 | AC | 52 | 0.138 | 0.184 | 85.00 | 2.48 | 55 | 34 |
| 8.5 | 24.245 | 0.191 | 0.058 | AC__8_5 | AC | 48 | 0.169 | 0.225 | 112.00 | 2.48 | 55 | 34 |
| 9.0 | 28.871 | 0.175 | 0.058 | AC__9_0 | AC | 44 | 0.199 | 0.265 | 139.00 | 2.48 | 55 | 34 |
| 9.5 | 25.603 | 0.181 | 0.061 | AC__9_5 | AC | 62 | 0.191 | 0.255 | 171.00 | 2.48 | 55 | 34 |
| 10.0 | 28.036 | 0.153 | 0.069 | AC__10_0 | AC | 80 | 0.183 | 0.244 | 203.00 | 2.48 | 55 | 34 |
| 10.5 | 25.148 | 0.132 | 0.089 | AC__10_5 | AC | 88 | 0.204 | 0.272 | 251.00 | 2.48 | 55 | 34 |
| 11.0 | 24.442 | 0.203 | 0.087 | AC__11_0 | AC | 96 | 0.225 | 0.300 | 299.00 | 2.48 | 55 | 34 |
| 11.5 | 6.894 | 0.084 | 0.085 | AC__11_5 | AC | 84 | 0.258 | 0.344 | 342.50 | 2.48 | 55 | 34 |
| 12.0 | 5.528 | 0.039 | 0.020 | AC__12_0 | AC | 72 | 0.290 | 0.387 | 386.00 | 2.48 | 55 | 34 |
| 12.5 | 6.805 | 0.052 | 0.097 | AC__12_5 | AC | 76 | 0.267 | 0.356 | 427.00 | 2.48 | 55 | 34 |
| 13.0 | 6.492 | 0.048 | 0.106 | AC__13_0 | AC | 80 | 0.244 | 0.325 | 468.00 | 2.48 | 55 | 34 |
| 13.5 | 8.541 | 0.032 | -0.043 | AC__13_5 | AC | 54 | 0.244 | 0.325 | 491.00 | 2.48 | 55 | 34 |
| 14.0 | 28.151 | 0.167 | -0.114 | AC__14_0 | AC | 28 | 0.243 | 0.324 | 514.00 | 2.48 | 55 | 34 |
| 14.5 | 23.779 | 0.187 | -0.067 | AC__14_5 | AC | 28 | 0.289 | 0.385 | 525.50 | 2.48 | 55 | 34 |
| 15.0 | 12.522 | 0.118 | -0.362 | AC__15_0 | AC | 28 | 0.334 | 0.445 | 537.00 | 2.48 | 55 | 34 |
| 15.5 | 6.764 | 0.043 | -0.339 | AC__15_5 | AC | 26 | 0.322 | 0.429 | 547.00 | 2.48 | 55 | 34 |
| 16.0 | 7.552 | 0.052 | -0.261 | AC__16_0 | AC | 24 | 0.310 | 0.413 | 557.00 | 2.48 | 55 | 34 |
| 16.5 | 5.400 | 0.022 | -0.145 | AC__16_5 | AC | 20 | 0.300 | 0.400 | 565.50 | 2.48 | 55 | 34 |
| 17.0 | 5.900 | 0.017 | -0.093 | AC__17_0 | AC | 16 | 0.290 | 0.387 | 574.00 | 2.48 | 55 | 34 |
| 17.5 | 4.048 | 0.024 | -0.144 | AC__17_5 | AC | 18 | 0.267 | 0.356 | 583.50 | 2.48 | 55 | 34 |
| 18.0 | 20.823 | 0.108 | -0.294 | AC__18_0 | AC | 20 | 0.244 | 0.326 | 593.00 | 2.48 | 55 | 34 |
| 18.5 | 27.700 | 0.225 | -0.238 | AC__18_5 | AC | 72 | 0.217 | 0.290 | 626.50 | 2.48 | 55 | 34 |
| 19.0 | 25.179 | 0.177 | -0.188 | AC__19_0 | AC | 124 | 0.190 | 0.254 | 660.00 | 2.48 | 55 | 34 |
| 19.5 | 22.926 | 0.151 | -0.069 | AC__19_5 | AC | 100 | 0.279 | 0.372 | 709.00 | 2.48 | 55 | 34 |
| 20.0 | 20.674 | 0.124 | 0.049 | AC__20_0 | AC | 76 | 0.368 | 0.490 | 758.00 | 2.48 | 55 | 34 |
| 20.5 | 25.997 | 0.231 | 0.185 | AC__20_5 | AC | 90 | 0.387 | 0.516 | 808.00 | 2.48 | 55 | 34 |
| 21.0 | 29.572 | 0.304 | 0.175 | AC__21_0 | AC | 104 | 0.407 | 0.543 | 858.00 | 2.48 | 55 | 34 |
| 21.5 | 40.627 | 0.408 | 0.158 | AC__21_5 | AC | 98 | 0.452 | 0.603 | 903.50 | 2.48 | 55 | 34 |
| 22.0 | 39.834 | 0.368 | 0.170 | AC__22_0 | AC | 92 | 0.498 | 0.664 | 949.00 | 2.48 | 55 | 34 |
| 22.5 | 34.681 | 0.356 | 0.163 | AC__22_5 | AC | 92 | 0.507 | 0.677 | 994.50 | 2.48 | 55 | 34 |
| 23.0 | 26.795 | 0.273 | 0.166 | AC__23_0 | AC | 92 | 0.517 | 0.690 | 1040.00 | 2.48 | 55 | 34 |
| 23.5 | 21.606 | 0.222 | 0.205 | AC__23_5 | AC | 90 | 0.518 | 0.691 | 1088.50 | 2.48 | 55 | 34 |
| 24.0 | 27.394 | 0.295 | 0.197 | AC__24_0 | AC | 88 | 0.519 | 0.692 | 1137.00 | 2.48 | 55 | 34 |
| 24.5 | 20.538 | 0.258 | 0.198 | AC__24_5 | AC | 94 | 0.531 | 0.709 | 1184.50 | 2.48 | 55 | 34 |
| 25.0 | 27.088 | 0.211 | -0.038 | AC__25_0 | AC | 100 | 0.544 | 0.725 | 1232.00 | 2.48 | 55 | 34 |
| 25.5 | 35.413 | 0.319 | 0.210 | AC__25_5 | AC | 98 | 0.560 | 0.746 | 1280.50 | 2.48 | 55 | 34 |
| 26.0 | 36.892 | 0.315 | 0.215 | AC__26_0 | AC | 96 | 0.576 | 0.768 | 1329.00 | 2.48 | 55 | 34 |
| 26.5 | 46.709 | 0.456 | 0.250 | AC__26_5 | AC | 94 | 0.582 | 0.776 | 1373.00 | 2.48 | 55 | 34 |
| 27.0 | 45.741 | 0.473 | 0.238 | AC__27_0 | AC | 92 | 0.588 | 0.784 | 1417.00 | 2.48 | 55 | 34 |
| 27.5 | 50.310 | 0.469 | 0.211 | AC__27_5 | AC | 92 | 0.593 | 0.790 | 1464.00 | 2.48 | 55 | 34 |
| 28.0 | 42.441 | 0.388 | 0.267 | AC__28_0 | AC | 92 | 0.597 | 0.796 | 1511.00 | 2.48 | 55 | 34 |
| 28.5 | 42.527 | 0.487 | 0.292 | AC__28_5 | AC | 94 | 0.586 | 0.781 | 1557.50 | 2.48 | 55 | 34 |
| 29.0 | 53.361 | 0.661 | 0.297 | AC__29_0 | AC | 96 | 0.574 | 0.766 | 1604.00 | 2.48 | 55 | 34 |
| 29.5 | 47.392 | 0.373 | 0.302 | AC__29_5 | AC | 108 | 0.516 | 0.689 | 1670.00 | 2.48 | 55 | 34 |
| 30.0 | 44.926 | 0.521 | 0.308 | AC__30_0 | AC | 120 | 0.459 | 0.612 | 1736.00 | 2.48 | 55 | 34 |
| 30.5 | 46.653 | 0.574 | 0.313 | AC__30_5 | AC | 110 | 0.502 | 0.670 | 1784.50 | 2.48 | 55 | 34 |
| 31.0 | 51.331 | 0.652 | 0.318 | AC__31_0 | AC | 100 | 0.546 | 0.728 | 1833.00 | 2.48 | 55 | 34 |
| 31.5 | 48.796 | 0.531 | 0.323 | AC__31_5 | AC | 98 | 0.562 | 0.750 | 1883.00 | 2.48 | 55 | 34 |
| 32.0 | 46.261 | 0.411 | 0.328 | AC__32_0 | AC | 96 | 0.579 | 0.772 | 1933.00 | 2.48 | 55 | 34 |
| 32.5 | 59.915 | 0.812 | 0.333 | AC__32_5 | AC | 94 | 0.589 | 0.786 | 1981.00 | 2.48 | 55 | 34 |
| 33.0 | 60.356 | 0.810 | 0.338 | AC__33_0 | AC | 92 | 0.600 | 0.800 | 2029.00 | 2.48 | 55 | 34 |
| 33.5 | 57.943 | 0.757 | 0.343 | AC__33_5 | AC | 102 | 0.591 | 0.788 | 2080.00 | 2.48 | 55 | 34 |
| 34.0 | 58.148 | 0.677 | 0.348 | AC__34_0 | AC | 112 | 0.583 | 0.777 | 2131.00 | 2.48 | 55 | 34 |
kable(top_n(validation, 100),
digits = 3,
caption = "Validation data.",
align = "r") %>%
kable_styling(font_size = 11) %>%
row_spec(0, color = "grey10", background = "#F4F5F6") %>%
scroll_box(height = "300px")| z [m] | qc [MPa] | fs [MPa] | u2 [MPa] | ID | Location ID | Normalised ENTRHU [-] | Normalised hammer energy [-] | Diameter [m] | Bottom wall thickness [mm] | Pile penetration [m] |
|---|---|---|---|---|---|---|---|---|---|---|
| 3.0 | 8.859 | 0.071 | 0.041 | CG__3_0 | CG | 0.120 | 0.160 | 2.48 | 50 | 33 |
| 3.5 | 12.484 | 0.111 | 0.041 | CG__3_5 | CG | 0.122 | 0.162 | 2.48 | 50 | 33 |
| 4.0 | 26.119 | 0.214 | 0.052 | CG__4_0 | CG | 0.124 | 0.165 | 2.48 | 50 | 33 |
| 4.5 | 31.922 | 0.298 | 0.066 | CG__4_5 | CG | 0.118 | 0.157 | 2.48 | 50 | 33 |
| 5.0 | 31.822 | 0.292 | 0.079 | CG__5_0 | CG | 0.112 | 0.149 | 2.48 | 50 | 33 |
| 5.5 | 29.064 | 0.268 | 0.090 | CG__5_5 | CG | 0.117 | 0.155 | 2.48 | 50 | 33 |
| 6.0 | 25.606 | 0.216 | 0.078 | CG__6_0 | CG | 0.121 | 0.162 | 2.48 | 50 | 33 |
| 6.5 | 28.187 | 0.251 | 0.062 | CG__6_5 | CG | 0.124 | 0.165 | 2.48 | 50 | 33 |
| 7.0 | 22.891 | 0.232 | 0.078 | CG__7_0 | CG | 0.126 | 0.168 | 2.48 | 50 | 33 |
| 7.5 | 22.283 | 0.199 | 0.090 | CG__7_5 | CG | 0.137 | 0.183 | 2.48 | 50 | 33 |
| 8.0 | 25.220 | 0.240 | 0.099 | CG__8_0 | CG | 0.148 | 0.198 | 2.48 | 50 | 33 |
| 8.5 | 24.819 | 0.244 | 0.107 | CG__8_5 | CG | 0.174 | 0.232 | 2.48 | 50 | 33 |
| 9.0 | 25.353 | 0.235 | 0.109 | CG__9_0 | CG | 0.200 | 0.267 | 2.48 | 50 | 33 |
| 9.5 | 29.594 | 0.261 | 0.101 | CG__9_5 | CG | 0.233 | 0.311 | 2.48 | 50 | 33 |
| 10.0 | 33.834 | 0.287 | 0.094 | CG__10_0 | CG | 0.267 | 0.356 | 2.48 | 50 | 33 |
| 10.5 | 27.980 | 0.143 | 0.075 | CG__10_5 | CG | 0.295 | 0.393 | 2.48 | 50 | 33 |
| 11.0 | 25.273 | 0.217 | 0.121 | CG__11_0 | CG | 0.323 | 0.431 | 2.48 | 50 | 33 |
| 11.5 | 14.002 | 0.148 | 0.132 | CG__11_5 | CG | 0.322 | 0.430 | 2.48 | 50 | 33 |
| 12.0 | 15.830 | 0.118 | 0.126 | CG__12_0 | CG | 0.322 | 0.429 | 2.48 | 50 | 33 |
| 12.5 | 11.260 | 0.110 | 0.143 | CG__12_5 | CG | 0.318 | 0.424 | 2.48 | 50 | 33 |
| 13.0 | 9.934 | 0.119 | 0.097 | CG__13_0 | CG | 0.314 | 0.419 | 2.48 | 50 | 33 |
| 13.5 | 18.624 | 0.144 | 0.126 | CG__13_5 | CG | 0.310 | 0.413 | 2.48 | 50 | 33 |
| 14.0 | 13.182 | 0.109 | 0.136 | CG__14_0 | CG | 0.306 | 0.408 | 2.48 | 50 | 33 |
| 14.5 | 10.605 | 0.092 | 0.126 | CG__14_5 | CG | 0.301 | 0.402 | 2.48 | 50 | 33 |
| 15.0 | 8.276 | 0.083 | 0.092 | CG__15_0 | CG | 0.297 | 0.396 | 2.48 | 50 | 33 |
| 15.5 | 14.220 | 0.140 | 0.132 | CG__15_5 | CG | 0.334 | 0.445 | 2.48 | 50 | 33 |
| 16.0 | 21.716 | 0.156 | 0.116 | CG__16_0 | CG | 0.371 | 0.494 | 2.48 | 50 | 33 |
| 16.5 | 19.388 | 0.134 | 0.129 | CG__16_5 | CG | 0.394 | 0.526 | 2.48 | 50 | 33 |
| 17.0 | 16.566 | 0.136 | 0.155 | CG__17_0 | CG | 0.418 | 0.558 | 2.48 | 50 | 33 |
| 17.5 | 22.589 | 0.174 | 0.172 | CG__17_5 | CG | 0.454 | 0.606 | 2.48 | 50 | 33 |
| 18.0 | 31.932 | 0.247 | 0.007 | CG__18_0 | CG | 0.491 | 0.654 | 2.48 | 50 | 33 |
| 18.5 | 50.331 | 0.298 | 0.192 | CG__18_5 | CG | 0.507 | 0.676 | 2.48 | 50 | 33 |
| 19.0 | 35.054 | 0.305 | 0.161 | CG__19_0 | CG | 0.524 | 0.698 | 2.48 | 50 | 33 |
| 19.5 | 38.511 | 0.366 | 0.179 | CG__19_5 | CG | 0.525 | 0.700 | 2.48 | 50 | 33 |
| 20.0 | 41.968 | 0.426 | 0.197 | CG__20_0 | CG | 0.526 | 0.702 | 2.48 | 50 | 33 |
| 20.5 | 43.108 | 0.460 | 0.196 | CG__20_5 | CG | 0.534 | 0.712 | 2.48 | 50 | 33 |
| 21.0 | 44.592 | 0.520 | 0.161 | CG__21_0 | CG | 0.541 | 0.722 | 2.48 | 50 | 33 |
| 21.5 | 47.682 | 0.567 | 0.155 | CG__21_5 | CG | 0.558 | 0.744 | 2.48 | 50 | 33 |
| 22.0 | 36.740 | 0.394 | -0.059 | CG__22_0 | CG | 0.574 | 0.766 | 2.48 | 50 | 33 |
| 22.5 | 30.662 | 0.268 | -0.259 | CG__22_5 | CG | 0.590 | 0.787 | 2.48 | 50 | 33 |
| 23.0 | 29.587 | 0.362 | -0.180 | CG__23_0 | CG | 0.606 | 0.808 | 2.48 | 50 | 33 |
| 23.5 | 33.710 | 0.410 | 0.222 | CG__23_5 | CG | 0.608 | 0.810 | 2.48 | 50 | 33 |
| 24.0 | 40.111 | 0.503 | 0.287 | CG__24_0 | CG | 0.610 | 0.813 | 2.48 | 50 | 33 |
| 24.5 | 40.079 | 0.511 | 0.291 | CG__24_5 | CG | 0.612 | 0.816 | 2.48 | 50 | 33 |
| 25.0 | 32.575 | 0.505 | 0.285 | CG__25_0 | CG | 0.614 | 0.819 | 2.48 | 50 | 33 |
| 25.5 | 25.071 | 0.499 | 0.279 | CG__25_5 | CG | 0.617 | 0.822 | 2.48 | 50 | 33 |
| 26.0 | 17.567 | 0.493 | 0.273 | CG__26_0 | CG | 0.619 | 0.826 | 2.48 | 50 | 33 |
| 26.5 | 23.495 | 0.550 | 0.272 | CG__26_5 | CG | 0.616 | 0.821 | 2.48 | 50 | 33 |
| 27.0 | 31.133 | 0.600 | 0.277 | CG__27_0 | CG | 0.612 | 0.816 | 2.48 | 50 | 33 |
| 27.5 | 33.581 | 0.524 | 0.282 | CG__27_5 | CG | 0.604 | 0.805 | 2.48 | 50 | 33 |
| 28.0 | 29.700 | 0.528 | 0.287 | CG__28_0 | CG | 0.596 | 0.794 | 2.48 | 50 | 33 |
| 28.5 | 30.296 | 0.590 | 0.292 | CG__28_5 | CG | 0.594 | 0.792 | 2.48 | 50 | 33 |
| 29.0 | 29.597 | 0.565 | 0.297 | CG__29_0 | CG | 0.593 | 0.791 | 2.48 | 50 | 33 |
| 29.5 | 32.513 | 0.582 | 0.302 | CG__29_5 | CG | 0.592 | 0.790 | 2.48 | 50 | 33 |
| 30.0 | 35.430 | 0.600 | 0.308 | CG__30_0 | CG | 0.591 | 0.788 | 2.48 | 50 | 33 |
| 30.5 | 48.335 | 0.804 | 0.313 | CG__30_5 | CG | 0.592 | 0.790 | 2.48 | 50 | 33 |
| 31.0 | 30.961 | 0.550 | 0.318 | CG__31_0 | CG | 0.593 | 0.791 | 2.48 | 50 | 33 |
| 31.5 | 29.890 | 0.543 | 0.323 | CG__31_5 | CG | 0.595 | 0.794 | 2.48 | 50 | 33 |
| 32.0 | 22.136 | 0.416 | 0.328 | CG__32_0 | CG | 0.598 | 0.797 | 2.48 | 50 | 33 |
| 4.0 | 26.119 | 0.214 | 0.052 | CH__4_0 | CH | 0.126 | 0.168 | 2.48 | 50 | 33 |
| 4.5 | 31.922 | 0.298 | 0.066 | CH__4_5 | CH | 0.122 | 0.162 | 2.48 | 50 | 33 |
| 5.0 | 31.822 | 0.292 | 0.079 | CH__5_0 | CH | 0.117 | 0.156 | 2.48 | 50 | 33 |
| 5.5 | 29.064 | 0.268 | 0.090 | CH__5_5 | CH | 0.117 | 0.156 | 2.48 | 50 | 33 |
| 6.0 | 25.606 | 0.216 | 0.078 | CH__6_0 | CH | 0.118 | 0.157 | 2.48 | 50 | 33 |
| 6.5 | 28.187 | 0.251 | 0.062 | CH__6_5 | CH | 0.140 | 0.186 | 2.48 | 50 | 33 |
| 7.0 | 22.891 | 0.232 | 0.078 | CH__7_0 | CH | 0.162 | 0.216 | 2.48 | 50 | 33 |
| 7.5 | 22.283 | 0.199 | 0.090 | CH__7_5 | CH | 0.175 | 0.233 | 2.48 | 50 | 33 |
| 8.0 | 25.220 | 0.240 | 0.099 | CH__8_0 | CH | 0.188 | 0.251 | 2.48 | 50 | 33 |
| 8.5 | 24.819 | 0.244 | 0.107 | CH__8_5 | CH | 0.188 | 0.251 | 2.48 | 50 | 33 |
| 9.0 | 25.353 | 0.235 | 0.109 | CH__9_0 | CH | 0.189 | 0.252 | 2.48 | 50 | 33 |
| 9.5 | 29.594 | 0.261 | 0.101 | CH__9_5 | CH | 0.232 | 0.309 | 2.48 | 50 | 33 |
| 10.0 | 33.834 | 0.287 | 0.094 | CH__10_0 | CH | 0.275 | 0.367 | 2.48 | 50 | 33 |
| 10.5 | 27.980 | 0.143 | 0.075 | CH__10_5 | CH | 0.295 | 0.393 | 2.48 | 50 | 33 |
| 11.0 | 25.273 | 0.217 | 0.121 | CH__11_0 | CH | 0.314 | 0.419 | 2.48 | 50 | 33 |
| 11.5 | 14.002 | 0.148 | 0.132 | CH__11_5 | CH | 0.312 | 0.416 | 2.48 | 50 | 33 |
| 12.0 | 15.830 | 0.118 | 0.126 | CH__12_0 | CH | 0.310 | 0.413 | 2.48 | 50 | 33 |
| 12.5 | 11.260 | 0.110 | 0.143 | CH__12_5 | CH | 0.314 | 0.419 | 2.48 | 50 | 33 |
| 13.0 | 9.934 | 0.119 | 0.097 | CH__13_0 | CH | 0.318 | 0.424 | 2.48 | 50 | 33 |
| 13.5 | 18.624 | 0.144 | 0.126 | CH__13_5 | CH | 0.314 | 0.419 | 2.48 | 50 | 33 |
| 14.0 | 13.182 | 0.109 | 0.136 | CH__14_0 | CH | 0.311 | 0.414 | 2.48 | 50 | 33 |
| 14.5 | 10.605 | 0.092 | 0.126 | CH__14_5 | CH | 0.305 | 0.407 | 2.48 | 50 | 33 |
| 15.0 | 8.276 | 0.083 | 0.092 | CH__15_0 | CH | 0.300 | 0.400 | 2.48 | 50 | 33 |
| 15.5 | 14.220 | 0.140 | 0.132 | CH__15_5 | CH | 0.329 | 0.439 | 2.48 | 50 | 33 |
| 16.0 | 21.716 | 0.156 | 0.116 | CH__16_0 | CH | 0.359 | 0.478 | 2.48 | 50 | 33 |
| 16.5 | 19.388 | 0.134 | 0.129 | CH__16_5 | CH | 0.392 | 0.523 | 2.48 | 50 | 33 |
| 17.0 | 16.566 | 0.136 | 0.155 | CH__17_0 | CH | 0.426 | 0.568 | 2.48 | 50 | 33 |
| 17.5 | 22.589 | 0.174 | 0.172 | CH__17_5 | CH | 0.478 | 0.637 | 2.48 | 50 | 33 |
| 18.0 | 31.932 | 0.247 | 0.007 | CH__18_0 | CH | 0.529 | 0.706 | 2.48 | 50 | 33 |
| 18.5 | 50.331 | 0.298 | 0.192 | CH__18_5 | CH | 0.525 | 0.700 | 2.48 | 50 | 33 |
| 19.0 | 35.054 | 0.305 | 0.161 | CH__19_0 | CH | 0.521 | 0.695 | 2.48 | 50 | 33 |
| 19.5 | 38.511 | 0.366 | 0.179 | CH__19_5 | CH | 0.517 | 0.690 | 2.48 | 50 | 33 |
| 20.0 | 41.968 | 0.426 | 0.197 | CH__20_0 | CH | 0.513 | 0.684 | 2.48 | 50 | 33 |
| 20.5 | 43.108 | 0.460 | 0.196 | CH__20_5 | CH | 0.512 | 0.682 | 2.48 | 50 | 33 |
| 21.0 | 44.592 | 0.520 | 0.161 | CH__21_0 | CH | 0.510 | 0.680 | 2.48 | 50 | 33 |
| 21.5 | 47.682 | 0.567 | 0.155 | CH__21_5 | CH | 0.510 | 0.680 | 2.48 | 50 | 33 |
| 22.0 | 36.740 | 0.394 | -0.059 | CH__22_0 | CH | 0.510 | 0.680 | 2.48 | 50 | 33 |
| 22.5 | 30.662 | 0.268 | -0.259 | CH__22_5 | CH | 0.529 | 0.705 | 2.48 | 50 | 33 |
| 23.0 | 29.587 | 0.362 | -0.180 | CH__23_0 | CH | 0.548 | 0.731 | 2.48 | 50 | 33 |
| 23.5 | 33.710 | 0.410 | 0.222 | CH__23_5 | CH | 0.554 | 0.738 | 2.48 | 50 | 33 |
| 24.0 | 40.111 | 0.503 | 0.287 | CH__24_0 | CH | 0.559 | 0.746 | 2.48 | 50 | 33 |
| 24.5 | 40.079 | 0.511 | 0.291 | CH__24_5 | CH | 0.561 | 0.748 | 2.48 | 50 | 33 |
| 25.0 | 32.575 | 0.505 | 0.285 | CH__25_0 | CH | 0.563 | 0.751 | 2.48 | 50 | 33 |
| 25.5 | 25.071 | 0.499 | 0.279 | CH__25_5 | CH | 0.567 | 0.756 | 2.48 | 50 | 33 |
| 26.0 | 17.567 | 0.493 | 0.273 | CH__26_0 | CH | 0.571 | 0.761 | 2.48 | 50 | 33 |
| 26.5 | 23.495 | 0.550 | 0.272 | CH__26_5 | CH | 0.576 | 0.768 | 2.48 | 50 | 33 |
| 27.0 | 31.133 | 0.600 | 0.277 | CH__27_0 | CH | 0.581 | 0.775 | 2.48 | 50 | 33 |
| 27.5 | 33.581 | 0.524 | 0.282 | CH__27_5 | CH | 0.587 | 0.782 | 2.48 | 50 | 33 |
| 28.0 | 29.700 | 0.528 | 0.287 | CH__28_0 | CH | 0.592 | 0.789 | 2.48 | 50 | 33 |
| 28.5 | 30.296 | 0.590 | 0.292 | CH__28_5 | CH | 0.598 | 0.797 | 2.48 | 50 | 33 |
| 29.0 | 29.597 | 0.565 | 0.297 | CH__29_0 | CH | 0.603 | 0.804 | 2.48 | 50 | 33 |
| 29.5 | 32.513 | 0.582 | 0.302 | CH__29_5 | CH | 0.601 | 0.801 | 2.48 | 50 | 33 |
| 30.0 | 35.430 | 0.600 | 0.308 | CH__30_0 | CH | 0.599 | 0.798 | 2.48 | 50 | 33 |
| 30.5 | 48.335 | 0.804 | 0.313 | CH__30_5 | CH | 0.597 | 0.795 | 2.48 | 50 | 33 |
| 31.0 | 30.961 | 0.550 | 0.318 | CH__31_0 | CH | 0.594 | 0.792 | 2.48 | 50 | 33 |
| 31.5 | 29.890 | 0.543 | 0.323 | CH__31_5 | CH | 0.594 | 0.792 | 2.48 | 50 | 33 |
| 32.0 | 22.136 | 0.416 | 0.328 | CH__32_0 | CH | 0.594 | 0.792 | 2.48 | 50 | 33 |
| 32.5 | 62.235 | 0.873 | 0.333 | CH__32_5 | CH | 0.585 | 0.780 | 2.48 | 50 | 33 |
| 33.0 | 77.961 | 1.010 | 0.338 | CH__33_0 | CH | 0.577 | 0.769 | 2.48 | 50 | 33 |
| 5.0 | 31.822 | 0.292 | 0.079 | CI__5_0 | CI | 0.121 | 0.162 | 2.48 | 50 | 33 |
| 5.5 | 29.064 | 0.268 | 0.090 | CI__5_5 | CI | 0.122 | 0.163 | 2.48 | 50 | 33 |
| 6.0 | 25.606 | 0.216 | 0.078 | CI__6_0 | CI | 0.124 | 0.165 | 2.48 | 50 | 33 |
| 6.5 | 28.187 | 0.251 | 0.062 | CI__6_5 | CI | 0.122 | 0.162 | 2.48 | 50 | 33 |
| 7.0 | 22.891 | 0.232 | 0.078 | CI__7_0 | CI | 0.119 | 0.159 | 2.48 | 50 | 33 |
| 7.5 | 22.283 | 0.199 | 0.090 | CI__7_5 | CI | 0.116 | 0.155 | 2.48 | 50 | 33 |
| 8.0 | 25.220 | 0.240 | 0.099 | CI__8_0 | CI | 0.113 | 0.151 | 2.48 | 50 | 33 |
| 8.5 | 24.819 | 0.244 | 0.107 | CI__8_5 | CI | 0.146 | 0.195 | 2.48 | 50 | 33 |
| 9.0 | 25.353 | 0.235 | 0.109 | CI__9_0 | CI | 0.179 | 0.239 | 2.48 | 50 | 33 |
| 9.5 | 29.594 | 0.261 | 0.101 | CI__9_5 | CI | 0.190 | 0.254 | 2.48 | 50 | 33 |
| 10.0 | 33.834 | 0.287 | 0.094 | CI__10_0 | CI | 0.201 | 0.268 | 2.48 | 50 | 33 |
| 10.5 | 27.980 | 0.143 | 0.075 | CI__10_5 | CI | 0.244 | 0.325 | 2.48 | 50 | 33 |
| 11.0 | 25.273 | 0.217 | 0.121 | CI__11_0 | CI | 0.287 | 0.382 | 2.48 | 50 | 33 |
| 11.5 | 14.002 | 0.148 | 0.132 | CI__11_5 | CI | 0.288 | 0.384 | 2.48 | 50 | 33 |
| 12.0 | 15.830 | 0.118 | 0.126 | CI__12_0 | CI | 0.289 | 0.386 | 2.48 | 50 | 33 |
| 12.5 | 11.260 | 0.110 | 0.143 | CI__12_5 | CI | 0.289 | 0.385 | 2.48 | 50 | 33 |
| 13.0 | 9.934 | 0.119 | 0.097 | CI__13_0 | CI | 0.288 | 0.384 | 2.48 | 50 | 33 |
| 13.5 | 18.624 | 0.144 | 0.126 | CI__13_5 | CI | 0.290 | 0.387 | 2.48 | 50 | 33 |
| 14.0 | 13.182 | 0.109 | 0.136 | CI__14_0 | CI | 0.292 | 0.389 | 2.48 | 50 | 33 |
| 14.5 | 10.605 | 0.092 | 0.126 | CI__14_5 | CI | 0.298 | 0.398 | 2.48 | 50 | 33 |
| 15.0 | 8.276 | 0.083 | 0.092 | CI__15_0 | CI | 0.305 | 0.407 | 2.48 | 50 | 33 |
| 15.5 | 14.220 | 0.140 | 0.132 | CI__15_5 | CI | 0.342 | 0.456 | 2.48 | 50 | 33 |
| 16.0 | 21.716 | 0.156 | 0.116 | CI__16_0 | CI | 0.379 | 0.505 | 2.48 | 50 | 33 |
| 16.5 | 19.388 | 0.134 | 0.129 | CI__16_5 | CI | 0.401 | 0.534 | 2.48 | 50 | 33 |
| 17.0 | 16.566 | 0.136 | 0.155 | CI__17_0 | CI | 0.422 | 0.563 | 2.48 | 50 | 33 |
| 17.5 | 22.589 | 0.174 | 0.172 | CI__17_5 | CI | 0.434 | 0.579 | 2.48 | 50 | 33 |
| 18.0 | 31.932 | 0.247 | 0.007 | CI__18_0 | CI | 0.446 | 0.595 | 2.48 | 50 | 33 |
| 18.5 | 50.331 | 0.298 | 0.192 | CI__18_5 | CI | 0.453 | 0.605 | 2.48 | 50 | 33 |
| 19.0 | 35.054 | 0.305 | 0.161 | CI__19_0 | CI | 0.461 | 0.614 | 2.48 | 50 | 33 |
| 19.5 | 38.511 | 0.366 | 0.179 | CI__19_5 | CI | 0.490 | 0.654 | 2.48 | 50 | 33 |
| 20.0 | 41.968 | 0.426 | 0.197 | CI__20_0 | CI | 0.520 | 0.693 | 2.48 | 50 | 33 |
| 20.5 | 43.108 | 0.460 | 0.196 | CI__20_5 | CI | 0.518 | 0.690 | 2.48 | 50 | 33 |
| 21.0 | 44.592 | 0.520 | 0.161 | CI__21_0 | CI | 0.515 | 0.687 | 2.48 | 50 | 33 |
| 21.5 | 47.682 | 0.567 | 0.155 | CI__21_5 | CI | 0.514 | 0.686 | 2.48 | 50 | 33 |
| 22.0 | 36.740 | 0.394 | -0.059 | CI__22_0 | CI | 0.514 | 0.685 | 2.48 | 50 | 33 |
| 22.5 | 30.662 | 0.268 | -0.259 | CI__22_5 | CI | 0.509 | 0.679 | 2.48 | 50 | 33 |
| 23.0 | 29.587 | 0.362 | -0.180 | CI__23_0 | CI | 0.505 | 0.673 | 2.48 | 50 | 33 |
| 23.5 | 33.710 | 0.410 | 0.222 | CI__23_5 | CI | 0.514 | 0.685 | 2.48 | 50 | 33 |
| 24.0 | 40.111 | 0.503 | 0.287 | CI__24_0 | CI | 0.522 | 0.697 | 2.48 | 50 | 33 |
| 24.5 | 40.079 | 0.511 | 0.291 | CI__24_5 | CI | 0.549 | 0.732 | 2.48 | 50 | 33 |
| 25.0 | 32.575 | 0.505 | 0.285 | CI__25_0 | CI | 0.576 | 0.768 | 2.48 | 50 | 33 |
| 25.5 | 25.071 | 0.499 | 0.279 | CI__25_5 | CI | 0.574 | 0.765 | 2.48 | 50 | 33 |
| 26.0 | 17.567 | 0.493 | 0.273 | CI__26_0 | CI | 0.571 | 0.762 | 2.48 | 50 | 33 |
| 26.5 | 23.495 | 0.550 | 0.272 | CI__26_5 | CI | 0.570 | 0.760 | 2.48 | 50 | 33 |
| 27.0 | 31.133 | 0.600 | 0.277 | CI__27_0 | CI | 0.568 | 0.758 | 2.48 | 50 | 33 |
| 27.5 | 33.581 | 0.524 | 0.282 | CI__27_5 | CI | 0.581 | 0.775 | 2.48 | 50 | 33 |
| 28.0 | 29.700 | 0.528 | 0.287 | CI__28_0 | CI | 0.594 | 0.792 | 2.48 | 50 | 33 |
| 28.5 | 30.296 | 0.590 | 0.292 | CI__28_5 | CI | 0.596 | 0.794 | 2.48 | 50 | 33 |
| 29.0 | 29.597 | 0.565 | 0.297 | CI__29_0 | CI | 0.597 | 0.796 | 2.48 | 50 | 33 |
| 29.5 | 32.513 | 0.582 | 0.302 | CI__29_5 | CI | 0.596 | 0.795 | 2.48 | 50 | 33 |
| 30.0 | 35.430 | 0.600 | 0.308 | CI__30_0 | CI | 0.596 | 0.794 | 2.48 | 50 | 33 |
| 30.5 | 48.335 | 0.804 | 0.313 | CI__30_5 | CI | 0.608 | 0.811 | 2.48 | 50 | 33 |
| 31.0 | 30.961 | 0.550 | 0.318 | CI__31_0 | CI | 0.621 | 0.828 | 2.48 | 50 | 33 |
| 31.5 | 29.890 | 0.543 | 0.323 | CI__31_5 | CI | 0.619 | 0.825 | 2.48 | 50 | 33 |
| 32.0 | 22.136 | 0.416 | 0.328 | CI__32_0 | CI | 0.618 | 0.823 | 2.48 | 50 | 33 |
| 32.5 | 62.235 | 0.873 | 0.333 | CI__32_5 | CI | 0.615 | 0.820 | 2.48 | 50 | 33 |
| 33.0 | 77.961 | 1.010 | 0.338 | CI__33_0 | CI | 0.612 | 0.817 | 2.48 | 50 | 33 |
The above tables show the first 100 rows of the training and validation data. The two datasets have the same structure. The parameters included in the data can be divided into four categories:
z [m] - depth;qc [MPa] - cone tip resistance;fs [MPa - cone sleeve friction;u2 [MPa] - pore pressure;Diameter [m] - pile outer diameter;Bottom wall thickness [mm] - wall thickness at the bottom of the pile;Pile penetration [m] - final penetration of the pile below mudline;Normalised ENTRHU [-] - energy transmitted to the pile (normalised to be between 0 and 1);Normalised hammer energy [-] - energy provided by the hammer (normalised to be between 0 and 1);Blowcount [Blows/m] - number of blows required for an additional meter of pile penetration;Number of blows - total number of blows to reach the selected depth;Location ID - anonymized location ID;ID - a unique ID combining the location name and depth.To facilitate the model-building process, the data have been pre-processed to regular grid with a vertical spacing of 0.5m (notice the values of z [m]). In overall there are 4610 and 1008 data points in the training and validation datasets, respectively.
Notice that for the validation dataset, Blowcount [Blows/m] and Number of blows are not provided, as these are the parameters to be predicted by the model (i.e. outcome). The remaining 11 variables can potentially be used as input to build the predictive model (i.e. features).
In addition, normalised data for CPT registrations for both training and validation datasets are also provided. We load and view these data here but, at this moment, we are not going to detail them.
training_no <- read_csv("F:/isfog2020/training_data_withnormalised.csv")
validation_no <- read_csv("F:/isfog2020/validation_data_withnormalised.csv")kable(top_n(training_no, 100),
digits = 3,
caption = "Normalised training data.",
align = "r") %>%
kable_styling(font_size = 11) %>%
row_spec(0, color = "grey10", background = "#F4F5F6") %>%
scroll_box(height = "300px")| z [m] | qc [MPa] | fs [MPa] | u2 [MPa] | ID | Location ID | Blowcount [Blows/m] | Normalised ENTRHU [-] | Normalised hammer energy [-] | Number of blows | Diameter [m] | Bottom wall thickness [mm] | Pile penetration [m] | area ratio [-] | Push | Total unit weight [kN/m3] | Layer no | Vertical total stress [kPa] | Water pressure [kPa] | Vertical effective stress [kPa] | qt [MPa] | Delta u2 [MPa] | Rf [%] | Bq [-] | Qt [-] | Fr [%] | qnet [MPa] | Ic [-] |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 11.5 | 1.641 | 0.024 | 0.080 | CD__11_5 | CD | 28 | 0.228 | 0.304 | 224.0 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 218.5 | 117.875 | 100.625 | 1.661 | -0.038 | 1.433 | -0.026 | 14.338 | 1.650 | 1.443 | 2.724 |
| 25.0 | 6.689 | 0.296 | 0.550 | CD__25_0 | CD | 72 | 0.534 | 0.711 | 1102.0 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 475.0 | 256.250 | 218.750 | 6.826 | 0.294 | 4.333 | 0.046 | 29.034 | 4.657 | 6.351 | 2.756 |
| 25.5 | 4.287 | 0.121 | 0.665 | CD__25_5 | CD | 64 | 0.533 | 0.711 | 1131.5 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 484.5 | 261.375 | 223.125 | 4.454 | 0.404 | 2.712 | 0.102 | 17.789 | 3.043 | 3.969 | 2.798 |
| 26.0 | 4.829 | 0.160 | 1.263 | CD__26_0 | CD | 56 | 0.533 | 0.710 | 1161.0 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 494.0 | 266.500 | 227.500 | 5.145 | 0.996 | 3.114 | 0.214 | 20.444 | 3.444 | 4.651 | 2.784 |
| 26.5 | 5.456 | 0.128 | 1.912 | CD__26_5 | CD | 56 | 0.541 | 0.722 | 1190.5 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 503.5 | 271.625 | 231.875 | 5.934 | 1.640 | 2.162 | 0.302 | 23.418 | 2.363 | 5.430 | 2.627 |
| 27.0 | 5.487 | 0.105 | 0.113 | CD__27_0 | CD | 56 | 0.550 | 0.733 | 1220.0 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 513.0 | 276.750 | 236.250 | 5.515 | -0.164 | 1.911 | -0.033 | 21.172 | 2.107 | 5.002 | 2.633 |
| 10.5 | 2.046 | 0.060 | 0.133 | CA__10_5 | CA | 34 | 0.170 | 0.226 | 196.5 | 2.48 | 50 | 27 | 0.75 | 1 | 19 | 1 | 199.5 | 107.625 | 91.875 | 2.079 | 0.026 | 2.881 | 0.014 | 20.463 | 3.186 | 1.880 | 2.764 |
| 11.5 | 2.311 | 0.057 | 0.219 | CA__11_5 | CA | 32 | 0.173 | 0.230 | 230.0 | 2.48 | 50 | 27 | 0.75 | 1 | 19 | 1 | 218.5 | 117.875 | 100.625 | 2.366 | 0.102 | 2.396 | 0.047 | 21.342 | 2.640 | 2.148 | 2.698 |
| 27.5 | 4.705 | 0.126 | 0.179 | DW__27_5 | DW | 100 | 0.386 | 0.514 | 1934.0 | 2.48 | 50 | 32 | 0.75 | 1 | 19 | 1 | 522.5 | 281.875 | 240.625 | 4.750 | -0.102 | 2.652 | -0.024 | 17.567 | 2.980 | 4.227 | 2.797 |
| 10.5 | 3.111 | 0.114 | 0.262 | AU__10_5 | AU | 70 | 0.229 | 0.310 | 253.0 | 2.48 | 55 | 32 | 0.75 | 1 | 19 | 1 | 199.5 | 107.625 | 91.875 | 3.177 | 0.154 | 3.595 | 0.052 | 32.403 | 3.836 | 2.977 | 2.666 |
| 13.5 | 3.585 | 0.131 | -0.140 | EC__13_5 | EC | 24 | 0.328 | 0.438 | 163.0 | 2.48 | 70 | 27 | 0.75 | 1 | 19 | 1 | 256.5 | 138.375 | 118.125 | 3.550 | -0.278 | 3.695 | -0.084 | 27.886 | 3.983 | 3.294 | 2.720 |
| 15.0 | 2.971 | 0.062 | -0.284 | AR__15_0 | AR | 60 | 0.466 | 0.621 | 229.0 | 2.48 | 55 | 29 | 0.75 | 1 | 19 | 1 | 285.0 | 153.750 | 131.250 | 2.900 | -0.438 | 2.153 | -0.167 | 19.924 | 2.388 | 2.615 | 2.690 |
| 25.5 | 4.827 | 0.187 | 0.269 | CM__25_5 | CM | 72 | 0.533 | 0.711 | 1277.0 | 2.48 | 50 | 30 | 0.75 | 1 | 19 | 1 | 484.5 | 261.375 | 223.125 | 4.895 | 0.008 | 3.814 | 0.002 | 19.766 | 4.233 | 4.410 | 2.853 |
| 12.5 | 2.705 | 0.115 | -0.082 | CY__12_5 | CY | 26 | 0.233 | 0.310 | 170.0 | 2.48 | 70 | 25 | 0.75 | 1 | 19 | 1 | 237.5 | 128.125 | 109.375 | 2.685 | -0.210 | 4.298 | -0.086 | 22.377 | 4.715 | 2.448 | 2.842 |
| 11.5 | 3.228 | 0.098 | 0.126 | EE__11_5 | EE | 52 | 0.242 | 0.323 | 387.5 | 2.48 | 50 | 30 | 0.75 | 1 | 19 | 1 | 218.5 | 117.875 | 100.625 | 3.259 | 0.008 | 3.007 | 0.003 | 30.216 | 3.223 | 3.041 | 2.635 |
| 11.0 | 2.627 | 0.074 | 0.126 | DK__11_0 | DK | 20 | 0.237 | 0.316 | 121.0 | 2.48 | 50 | 31 | 0.75 | 1 | 19 | 1 | 209.0 | 112.750 | 96.250 | 2.658 | 0.013 | 2.795 | 0.005 | 25.448 | 3.033 | 2.449 | 2.677 |
| 1.0 | 0.347 | 0.016 | 0.025 | AL__1_0 | AL | NA | NA | NA | NA | 2.48 | 55 | 30 | 0.75 | 1 | 19 | 1 | 19.0 | 10.250 | 8.750 | 0.353 | 0.015 | 4.490 | 0.044 | 38.200 | 4.745 | 0.334 | 2.750 |
| 38.5 | 9.247 | 0.236 | -0.208 | BA__38_5 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 731.5 | 394.625 | 336.875 | 9.195 | -0.602 | 2.563 | -0.071 | 25.124 | 2.785 | 8.464 | 2.656 |
| 41.0 | 6.616 | 0.212 | -0.119 | BA__41_0 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 779.0 | 420.250 | 358.750 | 6.586 | -0.539 | 3.222 | -0.093 | 16.187 | 3.654 | 5.807 | 2.879 |
| 41.5 | 5.836 | 0.231 | 0.353 | BA__41_5 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 788.5 | 425.375 | 363.125 | 5.925 | -0.072 | 3.894 | -0.014 | 14.144 | 4.492 | 5.136 | 2.981 |
| 42.0 | 5.571 | 0.215 | 1.179 | BA__42_0 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 798.0 | 430.500 | 367.500 | 5.865 | 0.749 | 3.664 | 0.148 | 13.789 | 4.241 | 5.067 | 2.974 |
| 42.5 | 5.011 | 0.201 | 1.243 | BA__42_5 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 807.5 | 435.625 | 371.875 | 5.322 | 0.808 | 3.784 | 0.179 | 12.141 | 4.461 | 4.515 | 3.031 |
| 43.0 | 3.789 | 0.151 | 0.760 | BA__43_0 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 817.0 | 440.750 | 376.250 | 3.979 | 0.319 | 3.787 | 0.101 | 8.404 | 4.766 | 3.162 | 3.175 |
| 43.5 | 3.956 | 0.176 | 1.057 | BA__43_5 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 826.5 | 445.875 | 380.625 | 4.221 | 0.611 | 4.158 | 0.180 | 8.917 | 5.171 | 3.394 | 3.176 |
| 44.0 | 6.573 | 0.196 | 1.105 | BA__44_0 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 836.0 | 451.000 | 385.000 | 6.850 | 0.654 | 2.869 | 0.109 | 15.620 | 3.268 | 6.014 | 2.862 |
| 45.5 | 7.516 | 0.169 | -0.260 | BA__45_5 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 864.5 | 466.375 | 398.125 | 7.451 | -0.727 | 2.267 | -0.110 | 16.544 | 2.564 | 6.586 | 2.779 |
| 46.5 | 4.458 | 0.179 | -0.241 | BA__46_5 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 883.5 | 476.625 | 406.875 | 4.398 | -0.718 | 4.077 | -0.204 | 8.637 | 5.102 | 3.514 | 3.184 |
| 47.0 | 4.204 | 0.169 | 0.021 | BA__47_0 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 893.0 | 481.750 | 411.250 | 4.209 | -0.461 | 4.011 | -0.139 | 8.063 | 5.091 | 3.316 | 3.207 |
| 47.5 | 4.129 | 0.106 | 0.374 | BA__47_5 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 902.5 | 486.875 | 415.625 | 4.222 | -0.113 | 2.519 | -0.034 | 7.988 | 3.204 | 3.320 | 3.094 |
| 48.0 | 6.130 | 0.196 | -0.424 | BA__48_0 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 912.0 | 492.000 | 420.000 | 6.024 | -0.916 | 3.254 | -0.179 | 12.171 | 3.834 | 5.112 | 2.990 |
| 48.5 | 3.613 | 0.098 | 1.463 | BA__48_5 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 921.5 | 497.125 | 424.375 | 3.979 | 0.966 | 2.470 | 0.316 | 7.205 | 3.215 | 3.058 | 3.132 |
| 49.5 | 6.013 | 0.172 | -0.499 | BA__49_5 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 940.5 | 507.375 | 433.125 | 5.888 | -1.007 | 2.920 | -0.203 | 11.423 | 3.474 | 4.947 | 2.987 |
| 50.0 | 4.809 | 0.111 | 0.340 | BA__50_0 | BA | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 950.0 | 512.500 | 437.500 | 4.894 | -0.172 | 2.266 | -0.044 | 9.015 | 2.812 | 3.944 | 3.018 |
| 14.5 | 3.169 | 0.129 | 0.127 | DB__14_5 | DB | 44 | 0.237 | 0.315 | 209.0 | 2.48 | 50 | 26 | 0.75 | 1 | 19 | 1 | 275.5 | 148.625 | 126.875 | 3.201 | -0.021 | 4.024 | -0.007 | 23.058 | 4.403 | 2.925 | 2.812 |
| 11.5 | 1.641 | 0.024 | 0.080 | CE__11_5 | CE | 58 | 0.217 | 0.289 | 352.0 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 218.5 | 117.875 | 100.625 | 1.661 | -0.038 | 1.433 | -0.026 | 14.338 | 1.650 | 1.443 | 2.724 |
| 25.0 | 6.689 | 0.296 | 0.550 | CE__25_0 | CE | 72 | 0.576 | 0.768 | 1383.0 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 475.0 | 256.250 | 218.750 | 6.826 | 0.294 | 4.333 | 0.046 | 29.034 | 4.657 | 6.351 | 2.756 |
| 25.5 | 4.287 | 0.121 | 0.665 | CE__25_5 | CE | 76 | 0.569 | 0.759 | 1421.0 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 484.5 | 261.375 | 223.125 | 4.454 | 0.404 | 2.712 | 0.102 | 17.789 | 3.043 | 3.969 | 2.798 |
| 26.0 | 4.829 | 0.160 | 1.263 | CE__26_0 | CE | 80 | 0.562 | 0.750 | 1459.0 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 494.0 | 266.500 | 227.500 | 5.145 | 0.996 | 3.114 | 0.214 | 20.444 | 3.444 | 4.651 | 2.784 |
| 26.5 | 5.456 | 0.128 | 1.912 | CE__26_5 | CE | 78 | 0.565 | 0.753 | 1495.5 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 503.5 | 271.625 | 231.875 | 5.934 | 1.640 | 2.162 | 0.302 | 23.418 | 2.363 | 5.430 | 2.627 |
| 27.0 | 5.487 | 0.105 | 0.113 | CE__27_0 | CE | 76 | 0.567 | 0.756 | 1532.0 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 513.0 | 276.750 | 236.250 | 5.515 | -0.164 | 1.911 | -0.033 | 21.172 | 2.107 | 5.002 | 2.633 |
| 10.5 | 2.046 | 0.060 | 0.133 | CB__10_5 | CB | 32 | 0.159 | 0.212 | 142.5 | 2.48 | 50 | 27 | 0.75 | 1 | 19 | 1 | 199.5 | 107.625 | 91.875 | 2.079 | 0.026 | 2.881 | 0.014 | 20.463 | 3.186 | 1.880 | 2.764 |
| 11.5 | 2.311 | 0.057 | 0.219 | CB__11_5 | CB | 30 | 0.142 | 0.189 | 169.0 | 2.48 | 50 | 27 | 0.75 | 1 | 19 | 1 | 218.5 | 117.875 | 100.625 | 2.366 | 0.102 | 2.396 | 0.047 | 21.342 | 2.640 | 2.148 | 2.698 |
| 27.5 | 4.705 | 0.126 | 0.179 | DX__27_5 | DX | 90 | 0.487 | 0.649 | 1904.5 | 2.48 | 50 | 32 | 0.75 | 1 | 19 | 1 | 522.5 | 281.875 | 240.625 | 4.750 | -0.102 | 2.652 | -0.024 | 17.567 | 2.980 | 4.227 | 2.797 |
| 10.5 | 3.111 | 0.114 | 0.262 | AV__10_5 | AV | 28 | 0.317 | 0.422 | 295.5 | 2.48 | 55 | 32 | 0.75 | 1 | 19 | 1 | 199.5 | 107.625 | 91.875 | 3.177 | 0.154 | 3.595 | 0.052 | 32.403 | 3.836 | 2.977 | 2.666 |
| 15.0 | 2.971 | 0.062 | -0.284 | AS__15_0 | AS | 84 | 0.476 | 0.634 | 366.0 | 2.48 | 55 | 29 | 0.75 | 1 | 19 | 1 | 285.0 | 153.750 | 131.250 | 2.900 | -0.438 | 2.153 | -0.167 | 19.924 | 2.388 | 2.615 | 2.690 |
| 25.5 | 4.827 | 0.187 | 0.269 | CN__25_5 | CN | 88 | 0.491 | 0.655 | 1569.0 | 2.48 | 50 | 30 | 0.75 | 1 | 19 | 1 | 484.5 | 261.375 | 223.125 | 4.895 | 0.008 | 3.814 | 0.002 | 19.766 | 4.233 | 4.410 | 2.853 |
| 12.5 | 2.705 | 0.115 | -0.082 | CZ__12_5 | CZ | 38 | 0.181 | 0.242 | 175.5 | 2.48 | 70 | 25 | 0.75 | 1 | 19 | 1 | 237.5 | 128.125 | 109.375 | 2.685 | -0.210 | 4.298 | -0.086 | 22.377 | 4.715 | 2.448 | 2.842 |
| 11.5 | 3.228 | 0.098 | 0.126 | EF__11_5 | EF | 82 | 0.254 | 0.338 | 498.0 | 2.48 | 50 | 30 | 0.75 | 1 | 19 | 1 | 218.5 | 117.875 | 100.625 | 3.259 | 0.008 | 3.007 | 0.003 | 30.216 | 3.223 | 3.041 | 2.635 |
| 11.0 | 2.627 | 0.074 | 0.126 | DL__11_0 | DL | 24 | 0.223 | 0.297 | 259.0 | 2.48 | 50 | 31 | 0.75 | 1 | 19 | 1 | 209.0 | 112.750 | 96.250 | 2.658 | 0.013 | 2.795 | 0.005 | 25.448 | 3.033 | 2.449 | 2.677 |
| 1.0 | 0.347 | 0.016 | 0.025 | AM__1_0 | AM | NA | NA | NA | NA | 2.48 | 55 | 30 | 0.75 | 1 | 19 | 1 | 19.0 | 10.250 | 8.750 | 0.353 | 0.015 | 4.490 | 0.044 | 38.200 | 4.745 | 0.334 | 2.750 |
| 38.5 | 9.247 | 0.236 | -0.208 | BB__38_5 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 731.5 | 394.625 | 336.875 | 9.195 | -0.602 | 2.563 | -0.071 | 25.124 | 2.785 | 8.464 | 2.656 |
| 41.0 | 6.616 | 0.212 | -0.119 | BB__41_0 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 779.0 | 420.250 | 358.750 | 6.586 | -0.539 | 3.222 | -0.093 | 16.187 | 3.654 | 5.807 | 2.879 |
| 41.5 | 5.836 | 0.231 | 0.353 | BB__41_5 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 788.5 | 425.375 | 363.125 | 5.925 | -0.072 | 3.894 | -0.014 | 14.144 | 4.492 | 5.136 | 2.981 |
| 42.0 | 5.571 | 0.215 | 1.179 | BB__42_0 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 798.0 | 430.500 | 367.500 | 5.865 | 0.749 | 3.664 | 0.148 | 13.789 | 4.241 | 5.067 | 2.974 |
| 42.5 | 5.011 | 0.201 | 1.243 | BB__42_5 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 807.5 | 435.625 | 371.875 | 5.322 | 0.808 | 3.784 | 0.179 | 12.141 | 4.461 | 4.515 | 3.031 |
| 43.0 | 3.789 | 0.151 | 0.760 | BB__43_0 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 817.0 | 440.750 | 376.250 | 3.979 | 0.319 | 3.787 | 0.101 | 8.404 | 4.766 | 3.162 | 3.175 |
| 43.5 | 3.956 | 0.176 | 1.057 | BB__43_5 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 826.5 | 445.875 | 380.625 | 4.221 | 0.611 | 4.158 | 0.180 | 8.917 | 5.171 | 3.394 | 3.176 |
| 44.0 | 6.573 | 0.196 | 1.105 | BB__44_0 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 836.0 | 451.000 | 385.000 | 6.850 | 0.654 | 2.869 | 0.109 | 15.620 | 3.268 | 6.014 | 2.862 |
| 45.5 | 7.516 | 0.169 | -0.260 | BB__45_5 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 864.5 | 466.375 | 398.125 | 7.451 | -0.727 | 2.267 | -0.110 | 16.544 | 2.564 | 6.586 | 2.779 |
| 46.5 | 4.458 | 0.179 | -0.241 | BB__46_5 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 883.5 | 476.625 | 406.875 | 4.398 | -0.718 | 4.077 | -0.204 | 8.637 | 5.102 | 3.514 | 3.184 |
| 47.0 | 4.204 | 0.169 | 0.021 | BB__47_0 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 893.0 | 481.750 | 411.250 | 4.209 | -0.461 | 4.011 | -0.139 | 8.063 | 5.091 | 3.316 | 3.207 |
| 47.5 | 4.129 | 0.106 | 0.374 | BB__47_5 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 902.5 | 486.875 | 415.625 | 4.222 | -0.113 | 2.519 | -0.034 | 7.988 | 3.204 | 3.320 | 3.094 |
| 48.0 | 6.130 | 0.196 | -0.424 | BB__48_0 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 912.0 | 492.000 | 420.000 | 6.024 | -0.916 | 3.254 | -0.179 | 12.171 | 3.834 | 5.112 | 2.990 |
| 48.5 | 3.613 | 0.098 | 1.463 | BB__48_5 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 921.5 | 497.125 | 424.375 | 3.979 | 0.966 | 2.470 | 0.316 | 7.205 | 3.215 | 3.058 | 3.132 |
| 49.5 | 6.013 | 0.172 | -0.499 | BB__49_5 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 940.5 | 507.375 | 433.125 | 5.888 | -1.007 | 2.920 | -0.203 | 11.423 | 3.474 | 4.947 | 2.987 |
| 50.0 | 4.809 | 0.111 | 0.340 | BB__50_0 | BB | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 950.0 | 512.500 | 437.500 | 4.894 | -0.172 | 2.266 | -0.044 | 9.015 | 2.812 | 3.944 | 3.018 |
| 14.5 | 3.169 | 0.129 | 0.127 | DC__14_5 | DC | 62 | 0.319 | 0.425 | 486.0 | 2.48 | 50 | 26 | 0.75 | 1 | 19 | 1 | 275.5 | 148.625 | 126.875 | 3.201 | -0.021 | 4.024 | -0.007 | 23.058 | 4.403 | 2.925 | 2.812 |
| 11.5 | 1.641 | 0.024 | 0.080 | CF__11_5 | CF | 34 | 0.268 | 0.357 | 295.5 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 218.5 | 117.875 | 100.625 | 1.661 | -0.038 | 1.433 | -0.026 | 14.338 | 1.650 | 1.443 | 2.724 |
| 25.0 | 6.689 | 0.296 | 0.550 | CF__25_0 | CF | 72 | 0.555 | 0.740 | 1252.0 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 475.0 | 256.250 | 218.750 | 6.826 | 0.294 | 4.333 | 0.046 | 29.034 | 4.657 | 6.351 | 2.756 |
| 25.5 | 4.287 | 0.121 | 0.665 | CF__25_5 | CF | 72 | 0.550 | 0.733 | 1289.5 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 484.5 | 261.375 | 223.125 | 4.454 | 0.404 | 2.712 | 0.102 | 17.789 | 3.043 | 3.969 | 2.798 |
| 26.0 | 4.829 | 0.160 | 1.263 | CF__26_0 | CF | 72 | 0.544 | 0.726 | 1327.0 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 494.0 | 266.500 | 227.500 | 5.145 | 0.996 | 3.114 | 0.214 | 20.444 | 3.444 | 4.651 | 2.784 |
| 26.5 | 5.456 | 0.128 | 1.912 | CF__26_5 | CF | 66 | 0.556 | 0.741 | 1361.5 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 503.5 | 271.625 | 231.875 | 5.934 | 1.640 | 2.162 | 0.302 | 23.418 | 2.363 | 5.430 | 2.627 |
| 27.0 | 5.487 | 0.105 | 0.113 | CF__27_0 | CF | 60 | 0.567 | 0.756 | 1396.0 | 2.48 | 70 | 32 | 0.75 | 1 | 19 | 1 | 513.0 | 276.750 | 236.250 | 5.515 | -0.164 | 1.911 | -0.033 | 21.172 | 2.107 | 5.002 | 2.633 |
| 10.5 | 2.046 | 0.060 | 0.133 | CC__10_5 | CC | 20 | 0.180 | 0.240 | 102.0 | 2.48 | 50 | 27 | 0.75 | 1 | 19 | 1 | 199.5 | 107.625 | 91.875 | 2.079 | 0.026 | 2.881 | 0.014 | 20.463 | 3.186 | 1.880 | 2.764 |
| 11.5 | 2.311 | 0.057 | 0.219 | CC__11_5 | CC | 20 | 0.164 | 0.218 | 124.5 | 2.48 | 50 | 27 | 0.75 | 1 | 19 | 1 | 218.5 | 117.875 | 100.625 | 2.366 | 0.102 | 2.396 | 0.047 | 21.342 | 2.640 | 2.148 | 2.698 |
| 27.5 | 4.705 | 0.126 | 0.179 | DY__27_5 | DY | 90 | 0.478 | 0.638 | 1988.5 | 2.48 | 50 | 32 | 0.75 | 1 | 19 | 1 | 522.5 | 281.875 | 240.625 | 4.750 | -0.102 | 2.652 | -0.024 | 17.567 | 2.980 | 4.227 | 2.797 |
| 10.5 | 3.111 | 0.114 | 0.262 | AW__10_5 | AW | 46 | 0.301 | 0.402 | 337.0 | 2.48 | 55 | 32 | 0.75 | 1 | 19 | 1 | 199.5 | 107.625 | 91.875 | 3.177 | 0.154 | 3.595 | 0.052 | 32.403 | 3.836 | 2.977 | 2.666 |
| 13.5 | 3.585 | 0.131 | -0.140 | ED__13_5 | ED | 28 | 0.313 | 0.417 | 254.5 | 2.48 | 70 | 27 | 0.75 | 1 | 19 | 1 | 256.5 | 138.375 | 118.125 | 3.550 | -0.278 | 3.695 | -0.084 | 27.886 | 3.983 | 3.294 | 2.720 |
| 15.0 | 2.971 | 0.062 | -0.284 | AT__15_0 | AT | 72 | 0.478 | 0.638 | 353.0 | 2.48 | 55 | 29 | 0.75 | 1 | 19 | 1 | 285.0 | 153.750 | 131.250 | 2.900 | -0.438 | 2.153 | -0.167 | 19.924 | 2.388 | 2.615 | 2.690 |
| 25.5 | 4.827 | 0.187 | 0.269 | CO__25_5 | CO | 86 | 0.523 | 0.698 | 1482.5 | 2.48 | 50 | 30 | 0.75 | 1 | 19 | 1 | 484.5 | 261.375 | 223.125 | 4.895 | 0.008 | 3.814 | 0.002 | 19.766 | 4.233 | 4.410 | 2.853 |
| 12.5 | 2.705 | 0.115 | -0.082 | DA__12_5 | DA | 24 | 0.218 | 0.290 | 143.0 | 2.48 | 70 | 25 | 0.75 | 1 | 19 | 1 | 237.5 | 128.125 | 109.375 | 2.685 | -0.210 | 4.298 | -0.086 | 22.377 | 4.715 | 2.448 | 2.842 |
| 11.5 | 3.228 | 0.098 | 0.126 | EG__11_5 | EG | 50 | 0.262 | 0.349 | 310.5 | 2.48 | 50 | 30 | 0.75 | 1 | 19 | 1 | 218.5 | 117.875 | 100.625 | 3.259 | 0.008 | 3.007 | 0.003 | 30.216 | 3.223 | 3.041 | 2.635 |
| 11.0 | 2.627 | 0.074 | 0.126 | DM__11_0 | DM | 24 | 0.243 | 0.324 | 225.0 | 2.48 | 50 | 31 | 0.75 | 1 | 19 | 1 | 209.0 | 112.750 | 96.250 | 2.658 | 0.013 | 2.795 | 0.005 | 25.448 | 3.033 | 2.449 | 2.677 |
| 1.0 | 0.347 | 0.016 | 0.025 | AN__1_0 | AN | NA | NA | NA | NA | 2.48 | 55 | 30 | 0.75 | 1 | 19 | 1 | 19.0 | 10.250 | 8.750 | 0.353 | 0.015 | 4.490 | 0.044 | 38.200 | 4.745 | 0.334 | 2.750 |
| 38.5 | 9.247 | 0.236 | -0.208 | BC__38_5 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 731.5 | 394.625 | 336.875 | 9.195 | -0.602 | 2.563 | -0.071 | 25.124 | 2.785 | 8.464 | 2.656 |
| 41.0 | 6.616 | 0.212 | -0.119 | BC__41_0 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 779.0 | 420.250 | 358.750 | 6.586 | -0.539 | 3.222 | -0.093 | 16.187 | 3.654 | 5.807 | 2.879 |
| 41.5 | 5.836 | 0.231 | 0.353 | BC__41_5 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 788.5 | 425.375 | 363.125 | 5.925 | -0.072 | 3.894 | -0.014 | 14.144 | 4.492 | 5.136 | 2.981 |
| 42.0 | 5.571 | 0.215 | 1.179 | BC__42_0 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 798.0 | 430.500 | 367.500 | 5.865 | 0.749 | 3.664 | 0.148 | 13.789 | 4.241 | 5.067 | 2.974 |
| 42.5 | 5.011 | 0.201 | 1.243 | BC__42_5 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 807.5 | 435.625 | 371.875 | 5.322 | 0.808 | 3.784 | 0.179 | 12.141 | 4.461 | 4.515 | 3.031 |
| 43.0 | 3.789 | 0.151 | 0.760 | BC__43_0 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 817.0 | 440.750 | 376.250 | 3.979 | 0.319 | 3.787 | 0.101 | 8.404 | 4.766 | 3.162 | 3.175 |
| 43.5 | 3.956 | 0.176 | 1.057 | BC__43_5 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 826.5 | 445.875 | 380.625 | 4.221 | 0.611 | 4.158 | 0.180 | 8.917 | 5.171 | 3.394 | 3.176 |
| 44.0 | 6.573 | 0.196 | 1.105 | BC__44_0 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 836.0 | 451.000 | 385.000 | 6.850 | 0.654 | 2.869 | 0.109 | 15.620 | 3.268 | 6.014 | 2.862 |
| 45.5 | 7.516 | 0.169 | -0.260 | BC__45_5 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 864.5 | 466.375 | 398.125 | 7.451 | -0.727 | 2.267 | -0.110 | 16.544 | 2.564 | 6.586 | 2.779 |
| 46.5 | 4.458 | 0.179 | -0.241 | BC__46_5 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 883.5 | 476.625 | 406.875 | 4.398 | -0.718 | 4.077 | -0.204 | 8.637 | 5.102 | 3.514 | 3.184 |
| 47.0 | 4.204 | 0.169 | 0.021 | BC__47_0 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 893.0 | 481.750 | 411.250 | 4.209 | -0.461 | 4.011 | -0.139 | 8.063 | 5.091 | 3.316 | 3.207 |
| 47.5 | 4.129 | 0.106 | 0.374 | BC__47_5 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 902.5 | 486.875 | 415.625 | 4.222 | -0.113 | 2.519 | -0.034 | 7.988 | 3.204 | 3.320 | 3.094 |
| 48.0 | 6.130 | 0.196 | -0.424 | BC__48_0 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 912.0 | 492.000 | 420.000 | 6.024 | -0.916 | 3.254 | -0.179 | 12.171 | 3.834 | 5.112 | 2.990 |
| 48.5 | 3.613 | 0.098 | 1.463 | BC__48_5 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 921.5 | 497.125 | 424.375 | 3.979 | 0.966 | 2.470 | 0.316 | 7.205 | 3.215 | 3.058 | 3.132 |
| 49.5 | 6.013 | 0.172 | -0.499 | BC__49_5 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 940.5 | 507.375 | 433.125 | 5.888 | -1.007 | 2.920 | -0.203 | 11.423 | 3.474 | 4.947 | 2.987 |
| 50.0 | 4.809 | 0.111 | 0.340 | BC__50_0 | BC | NA | NA | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 950.0 | 512.500 | 437.500 | 4.894 | -0.172 | 2.266 | -0.044 | 9.015 | 2.812 | 3.944 | 3.018 |
| 14.5 | 3.169 | 0.129 | 0.127 | DD__14_5 | DD | 42 | 0.366 | 0.488 | 356.5 | 2.48 | 50 | 26 | 0.75 | 1 | 19 | 1 | 275.5 | 148.625 | 126.875 | 3.201 | -0.021 | 4.024 | -0.007 | 23.058 | 4.403 | 2.925 | 2.812 |
kable(top_n(validation_no, 100),
digits = 3,
caption = "Normalised validation data.",
align = "r") %>%
kable_styling(font_size = 11) %>%
row_spec(0, color = "grey10", background = "#F4F5F6") %>%
scroll_box(height = "300px")| z [m] | qc [MPa] | fs [MPa] | u2 [MPa] | ID | Location ID | Normalised ENTRHU [-] | Normalised hammer energy [-] | Diameter [m] | Bottom wall thickness [mm] | Pile penetration [m] | area ratio [-] | Push | Total unit weight [kN/m3] | Layer no | Vertical total stress [kPa] | Water pressure [kPa] | Vertical effective stress [kPa] | qt [MPa] | Delta u2 [MPa] | Rf [%] | Bq [-] | Qt [-] | Fr [%] | qnet [MPa] | Ic [-] |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 32.5 | 7.863 | 0.224 | 0.906 | BX__32_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 617.5 | 333.125 | 284.375 | 8.089 | 0.573 | 2.773 | 0.077 | 26.275 | 3.003 | 7.472 | 2.662 |
| 33.0 | 3.015 | 0.050 | 1.318 | BX__33_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 627.0 | 338.250 | 288.750 | 3.344 | 0.980 | 1.491 | 0.361 | 9.411 | 1.835 | 2.718 | 2.904 |
| 33.5 | 2.712 | 0.038 | 1.438 | BX__33_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 636.5 | 343.375 | 293.125 | 3.072 | 1.095 | 1.228 | 0.450 | 8.307 | 1.549 | 2.435 | 2.914 |
| 34.0 | 3.168 | 0.069 | 0.041 | BX__34_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 646.0 | 348.500 | 297.500 | 3.178 | -0.308 | 2.184 | -0.121 | 8.512 | 2.741 | 2.532 | 3.033 |
| 36.0 | 5.779 | 0.215 | 0.123 | BX__36_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 684.0 | 369.000 | 315.000 | 5.810 | -0.246 | 3.693 | -0.048 | 16.272 | 4.186 | 5.126 | 2.914 |
| 36.5 | 8.903 | 0.267 | -0.316 | BX__36_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 693.5 | 374.125 | 319.375 | 8.824 | -0.690 | 3.022 | -0.085 | 25.457 | 3.280 | 8.130 | 2.697 |
| 40.0 | 10.292 | 0.257 | -0.386 | BX__40_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 760.0 | 410.000 | 350.000 | 10.195 | -0.796 | 2.517 | -0.084 | 26.958 | 2.720 | 9.435 | 2.626 |
| 40.5 | 3.886 | 0.079 | -0.395 | BX__40_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 769.5 | 415.125 | 354.375 | 3.787 | -0.810 | 2.085 | -0.269 | 8.516 | 2.617 | 3.018 | 3.022 |
| 41.0 | 3.027 | 0.046 | -0.380 | BX__41_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 779.0 | 420.250 | 358.750 | 2.932 | -0.800 | 1.583 | -0.372 | 6.001 | 2.156 | 2.153 | 3.108 |
| 41.5 | 4.140 | 0.140 | -0.362 | BX__41_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 788.5 | 425.375 | 363.125 | 4.049 | -0.788 | 3.452 | -0.242 | 8.980 | 4.287 | 3.261 | 3.125 |
| 42.0 | 4.848 | 0.141 | -0.216 | BX__42_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 798.0 | 430.500 | 367.500 | 4.794 | -0.646 | 2.944 | -0.162 | 10.872 | 3.532 | 3.996 | 3.008 |
| 42.5 | 5.117 | 0.194 | 0.067 | BX__42_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 807.5 | 435.625 | 371.875 | 5.134 | -0.369 | 3.781 | -0.085 | 11.634 | 4.487 | 4.327 | 3.047 |
| 43.0 | 5.387 | 0.247 | 0.349 | BX__43_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 817.0 | 440.750 | 376.250 | 5.474 | -0.091 | 4.514 | -0.020 | 12.379 | 5.306 | 4.657 | 3.071 |
| 43.5 | 7.043 | 0.235 | 0.580 | BX__43_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 826.5 | 445.875 | 380.625 | 7.188 | 0.134 | 3.274 | 0.021 | 16.713 | 3.699 | 6.361 | 2.872 |
| 44.5 | 8.339 | 0.251 | 0.085 | BX__44_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 845.5 | 456.125 | 389.375 | 8.360 | -0.371 | 2.997 | -0.049 | 19.299 | 3.334 | 7.515 | 2.795 |
| 45.0 | 8.044 | 0.239 | 0.279 | BX__45_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 855.0 | 461.250 | 393.750 | 8.114 | -0.182 | 2.947 | -0.025 | 18.435 | 3.294 | 7.259 | 2.807 |
| 45.5 | 4.854 | 0.206 | 0.189 | BX__45_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 864.5 | 466.375 | 398.125 | 4.901 | -0.278 | 4.195 | -0.069 | 10.139 | 5.093 | 4.037 | 3.128 |
| 46.0 | 3.901 | 0.183 | 0.697 | BX__46_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 874.0 | 471.500 | 402.500 | 4.076 | 0.225 | 4.482 | 0.070 | 7.954 | 5.705 | 3.202 | 3.242 |
| 46.5 | 4.080 | 0.236 | 0.684 | BX__46_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 883.5 | 476.625 | 406.875 | 4.251 | 0.208 | 5.548 | 0.062 | 8.277 | 7.004 | 3.368 | 3.283 |
| 47.0 | 4.199 | 0.195 | 0.450 | BX__47_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 893.0 | 481.750 | 411.250 | 4.312 | -0.031 | 4.515 | -0.009 | 8.313 | 5.694 | 3.419 | 3.226 |
| 47.5 | 4.333 | 0.198 | 0.348 | BX__47_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 902.5 | 486.875 | 415.625 | 4.420 | -0.138 | 4.475 | -0.039 | 8.463 | 5.623 | 3.518 | 3.216 |
| 48.0 | 4.478 | 0.263 | 1.248 | BX__48_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 912.0 | 492.000 | 420.000 | 4.790 | 0.756 | 5.486 | 0.195 | 9.233 | 6.776 | 3.878 | 3.237 |
| 48.5 | 4.854 | 0.256 | 2.002 | BX__48_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 921.5 | 497.125 | 424.375 | 5.355 | 1.505 | 4.778 | 0.340 | 10.446 | 5.771 | 4.433 | 3.152 |
| 49.0 | 5.238 | 0.210 | 2.240 | BX__49_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 931.0 | 502.250 | 428.750 | 5.798 | 1.738 | 3.630 | 0.357 | 11.351 | 4.324 | 4.867 | 3.046 |
| 49.5 | 4.987 | 0.199 | 2.080 | BX__49_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 940.5 | 507.375 | 433.125 | 5.507 | 1.572 | 3.615 | 0.344 | 10.543 | 4.359 | 4.566 | 3.073 |
| 50.0 | 4.736 | 0.188 | 1.919 | BX__50_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 950.0 | 512.500 | 437.500 | 5.216 | 1.406 | 3.598 | 0.330 | 9.751 | 4.400 | 4.266 | 3.103 |
| 50.5 | 8.496 | 0.234 | 0.502 | BX__50_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 959.5 | 517.625 | 441.875 | 8.621 | -0.016 | 2.709 | -0.002 | 17.339 | 3.048 | 7.662 | 2.807 |
| 51.0 | 3.739 | 0.102 | 2.134 | BX__51_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 969.0 | 522.750 | 446.250 | 4.272 | 1.611 | 2.386 | 0.488 | 7.403 | 3.086 | 3.303 | 3.112 |
| 51.5 | 4.661 | 0.199 | 0.870 | BX__51_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 978.5 | 527.875 | 450.625 | 4.878 | 0.342 | 4.087 | 0.088 | 8.654 | 5.113 | 3.900 | 3.184 |
| 52.0 | 11.025 | 0.235 | -0.570 | BX__52_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 988.0 | 533.000 | 455.000 | 10.882 | -1.103 | 2.161 | -0.112 | 21.746 | 2.377 | 9.894 | 2.664 |
| 52.5 | 14.213 | 0.437 | -0.415 | BX__52_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 997.5 | 538.125 | 459.375 | 14.109 | -0.954 | 3.101 | -0.073 | 28.542 | 3.337 | 13.112 | 2.664 |
| 54.0 | 11.391 | 0.420 | 0.123 | BX__54_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 1026.0 | 553.500 | 472.500 | 11.422 | -0.430 | 3.675 | -0.041 | 22.002 | 4.038 | 10.396 | 2.804 |
| 57.5 | 5.914 | 0.269 | -0.017 | BX__57_5 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 1092.5 | 589.375 | 503.125 | 5.910 | -0.606 | 4.559 | -0.126 | 9.575 | 5.593 | 4.817 | 3.173 |
| 58.0 | 10.613 | 0.352 | -0.069 | BX__58_0 | BX | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 1102.0 | 594.500 | 507.500 | 10.596 | -0.664 | 3.326 | -0.070 | 18.707 | 3.712 | 9.494 | 2.834 |
| 32.5 | 7.863 | 0.224 | 0.906 | BY__32_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 617.5 | 333.125 | 284.375 | 8.089 | 0.573 | 2.773 | 0.077 | 26.275 | 3.003 | 7.472 | 2.662 |
| 33.0 | 3.015 | 0.050 | 1.318 | BY__33_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 627.0 | 338.250 | 288.750 | 3.344 | 0.980 | 1.491 | 0.361 | 9.411 | 1.835 | 2.718 | 2.904 |
| 33.5 | 2.712 | 0.038 | 1.438 | BY__33_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 636.5 | 343.375 | 293.125 | 3.072 | 1.095 | 1.228 | 0.450 | 8.307 | 1.549 | 2.435 | 2.914 |
| 34.0 | 3.168 | 0.069 | 0.041 | BY__34_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 646.0 | 348.500 | 297.500 | 3.178 | -0.308 | 2.184 | -0.121 | 8.512 | 2.741 | 2.532 | 3.033 |
| 36.0 | 5.779 | 0.215 | 0.123 | BY__36_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 684.0 | 369.000 | 315.000 | 5.810 | -0.246 | 3.693 | -0.048 | 16.272 | 4.186 | 5.126 | 2.914 |
| 36.5 | 8.903 | 0.267 | -0.316 | BY__36_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 693.5 | 374.125 | 319.375 | 8.824 | -0.690 | 3.022 | -0.085 | 25.457 | 3.280 | 8.130 | 2.697 |
| 40.0 | 10.292 | 0.257 | -0.386 | BY__40_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 760.0 | 410.000 | 350.000 | 10.195 | -0.796 | 2.517 | -0.084 | 26.958 | 2.720 | 9.435 | 2.626 |
| 40.5 | 3.886 | 0.079 | -0.395 | BY__40_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 769.5 | 415.125 | 354.375 | 3.787 | -0.810 | 2.085 | -0.269 | 8.516 | 2.617 | 3.018 | 3.022 |
| 41.0 | 3.027 | 0.046 | -0.380 | BY__41_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 779.0 | 420.250 | 358.750 | 2.932 | -0.800 | 1.583 | -0.372 | 6.001 | 2.156 | 2.153 | 3.108 |
| 41.5 | 4.140 | 0.140 | -0.362 | BY__41_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 788.5 | 425.375 | 363.125 | 4.049 | -0.788 | 3.452 | -0.242 | 8.980 | 4.287 | 3.261 | 3.125 |
| 42.0 | 4.848 | 0.141 | -0.216 | BY__42_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 798.0 | 430.500 | 367.500 | 4.794 | -0.646 | 2.944 | -0.162 | 10.872 | 3.532 | 3.996 | 3.008 |
| 42.5 | 5.117 | 0.194 | 0.067 | BY__42_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 807.5 | 435.625 | 371.875 | 5.134 | -0.369 | 3.781 | -0.085 | 11.634 | 4.487 | 4.327 | 3.047 |
| 43.0 | 5.387 | 0.247 | 0.349 | BY__43_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 817.0 | 440.750 | 376.250 | 5.474 | -0.091 | 4.514 | -0.020 | 12.379 | 5.306 | 4.657 | 3.071 |
| 43.5 | 7.043 | 0.235 | 0.580 | BY__43_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 826.5 | 445.875 | 380.625 | 7.188 | 0.134 | 3.274 | 0.021 | 16.713 | 3.699 | 6.361 | 2.872 |
| 44.5 | 8.339 | 0.251 | 0.085 | BY__44_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 845.5 | 456.125 | 389.375 | 8.360 | -0.371 | 2.997 | -0.049 | 19.299 | 3.334 | 7.515 | 2.795 |
| 45.0 | 8.044 | 0.239 | 0.279 | BY__45_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 855.0 | 461.250 | 393.750 | 8.114 | -0.182 | 2.947 | -0.025 | 18.435 | 3.294 | 7.259 | 2.807 |
| 45.5 | 4.854 | 0.206 | 0.189 | BY__45_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 864.5 | 466.375 | 398.125 | 4.901 | -0.278 | 4.195 | -0.069 | 10.139 | 5.093 | 4.037 | 3.128 |
| 46.0 | 3.901 | 0.183 | 0.697 | BY__46_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 874.0 | 471.500 | 402.500 | 4.076 | 0.225 | 4.482 | 0.070 | 7.954 | 5.705 | 3.202 | 3.242 |
| 46.5 | 4.080 | 0.236 | 0.684 | BY__46_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 883.5 | 476.625 | 406.875 | 4.251 | 0.208 | 5.548 | 0.062 | 8.277 | 7.004 | 3.368 | 3.283 |
| 47.0 | 4.199 | 0.195 | 0.450 | BY__47_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 893.0 | 481.750 | 411.250 | 4.312 | -0.031 | 4.515 | -0.009 | 8.313 | 5.694 | 3.419 | 3.226 |
| 47.5 | 4.333 | 0.198 | 0.348 | BY__47_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 902.5 | 486.875 | 415.625 | 4.420 | -0.138 | 4.475 | -0.039 | 8.463 | 5.623 | 3.518 | 3.216 |
| 48.0 | 4.478 | 0.263 | 1.248 | BY__48_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 912.0 | 492.000 | 420.000 | 4.790 | 0.756 | 5.486 | 0.195 | 9.233 | 6.776 | 3.878 | 3.237 |
| 48.5 | 4.854 | 0.256 | 2.002 | BY__48_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 921.5 | 497.125 | 424.375 | 5.355 | 1.505 | 4.778 | 0.340 | 10.446 | 5.771 | 4.433 | 3.152 |
| 49.0 | 5.238 | 0.210 | 2.240 | BY__49_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 931.0 | 502.250 | 428.750 | 5.798 | 1.738 | 3.630 | 0.357 | 11.351 | 4.324 | 4.867 | 3.046 |
| 49.5 | 4.987 | 0.199 | 2.080 | BY__49_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 940.5 | 507.375 | 433.125 | 5.507 | 1.572 | 3.615 | 0.344 | 10.543 | 4.359 | 4.566 | 3.073 |
| 50.0 | 4.736 | 0.188 | 1.919 | BY__50_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 950.0 | 512.500 | 437.500 | 5.216 | 1.406 | 3.598 | 0.330 | 9.751 | 4.400 | 4.266 | 3.103 |
| 50.5 | 8.496 | 0.234 | 0.502 | BY__50_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 959.5 | 517.625 | 441.875 | 8.621 | -0.016 | 2.709 | -0.002 | 17.339 | 3.048 | 7.662 | 2.807 |
| 51.0 | 3.739 | 0.102 | 2.134 | BY__51_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 969.0 | 522.750 | 446.250 | 4.272 | 1.611 | 2.386 | 0.488 | 7.403 | 3.086 | 3.303 | 3.112 |
| 51.5 | 4.661 | 0.199 | 0.870 | BY__51_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 978.5 | 527.875 | 450.625 | 4.878 | 0.342 | 4.087 | 0.088 | 8.654 | 5.113 | 3.900 | 3.184 |
| 52.0 | 11.025 | 0.235 | -0.570 | BY__52_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 988.0 | 533.000 | 455.000 | 10.882 | -1.103 | 2.161 | -0.112 | 21.746 | 2.377 | 9.894 | 2.664 |
| 52.5 | 14.213 | 0.437 | -0.415 | BY__52_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 997.5 | 538.125 | 459.375 | 14.109 | -0.954 | 3.101 | -0.073 | 28.542 | 3.337 | 13.112 | 2.664 |
| 54.0 | 11.391 | 0.420 | 0.123 | BY__54_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 1026.0 | 553.500 | 472.500 | 11.422 | -0.430 | 3.675 | -0.041 | 22.002 | 4.038 | 10.396 | 2.804 |
| 57.5 | 5.914 | 0.269 | -0.017 | BY__57_5 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 1092.5 | 589.375 | 503.125 | 5.910 | -0.606 | 4.559 | -0.126 | 9.575 | 5.593 | 4.817 | 3.173 |
| 58.0 | 10.613 | 0.352 | -0.069 | BY__58_0 | BY | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 1102.0 | 594.500 | 507.500 | 10.596 | -0.664 | 3.326 | -0.070 | 18.707 | 3.712 | 9.494 | 2.834 |
| 32.5 | 7.863 | 0.224 | 0.906 | BZ__32_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 617.5 | 333.125 | 284.375 | 8.089 | 0.573 | 2.773 | 0.077 | 26.275 | 3.003 | 7.472 | 2.662 |
| 33.0 | 3.015 | 0.050 | 1.318 | BZ__33_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 627.0 | 338.250 | 288.750 | 3.344 | 0.980 | 1.491 | 0.361 | 9.411 | 1.835 | 2.718 | 2.904 |
| 33.5 | 2.712 | 0.038 | 1.438 | BZ__33_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 636.5 | 343.375 | 293.125 | 3.072 | 1.095 | 1.228 | 0.450 | 8.307 | 1.549 | 2.435 | 2.914 |
| 34.0 | 3.168 | 0.069 | 0.041 | BZ__34_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 646.0 | 348.500 | 297.500 | 3.178 | -0.308 | 2.184 | -0.121 | 8.512 | 2.741 | 2.532 | 3.033 |
| 36.0 | 5.779 | 0.215 | 0.123 | BZ__36_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 684.0 | 369.000 | 315.000 | 5.810 | -0.246 | 3.693 | -0.048 | 16.272 | 4.186 | 5.126 | 2.914 |
| 36.5 | 8.903 | 0.267 | -0.316 | BZ__36_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 693.5 | 374.125 | 319.375 | 8.824 | -0.690 | 3.022 | -0.085 | 25.457 | 3.280 | 8.130 | 2.697 |
| 40.0 | 10.292 | 0.257 | -0.386 | BZ__40_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 760.0 | 410.000 | 350.000 | 10.195 | -0.796 | 2.517 | -0.084 | 26.958 | 2.720 | 9.435 | 2.626 |
| 40.5 | 3.886 | 0.079 | -0.395 | BZ__40_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 769.5 | 415.125 | 354.375 | 3.787 | -0.810 | 2.085 | -0.269 | 8.516 | 2.617 | 3.018 | 3.022 |
| 41.0 | 3.027 | 0.046 | -0.380 | BZ__41_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 779.0 | 420.250 | 358.750 | 2.932 | -0.800 | 1.583 | -0.372 | 6.001 | 2.156 | 2.153 | 3.108 |
| 41.5 | 4.140 | 0.140 | -0.362 | BZ__41_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 788.5 | 425.375 | 363.125 | 4.049 | -0.788 | 3.452 | -0.242 | 8.980 | 4.287 | 3.261 | 3.125 |
| 42.0 | 4.848 | 0.141 | -0.216 | BZ__42_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 798.0 | 430.500 | 367.500 | 4.794 | -0.646 | 2.944 | -0.162 | 10.872 | 3.532 | 3.996 | 3.008 |
| 42.5 | 5.117 | 0.194 | 0.067 | BZ__42_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 807.5 | 435.625 | 371.875 | 5.134 | -0.369 | 3.781 | -0.085 | 11.634 | 4.487 | 4.327 | 3.047 |
| 43.0 | 5.387 | 0.247 | 0.349 | BZ__43_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 817.0 | 440.750 | 376.250 | 5.474 | -0.091 | 4.514 | -0.020 | 12.379 | 5.306 | 4.657 | 3.071 |
| 43.5 | 7.043 | 0.235 | 0.580 | BZ__43_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 826.5 | 445.875 | 380.625 | 7.188 | 0.134 | 3.274 | 0.021 | 16.713 | 3.699 | 6.361 | 2.872 |
| 44.5 | 8.339 | 0.251 | 0.085 | BZ__44_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 845.5 | 456.125 | 389.375 | 8.360 | -0.371 | 2.997 | -0.049 | 19.299 | 3.334 | 7.515 | 2.795 |
| 45.0 | 8.044 | 0.239 | 0.279 | BZ__45_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 855.0 | 461.250 | 393.750 | 8.114 | -0.182 | 2.947 | -0.025 | 18.435 | 3.294 | 7.259 | 2.807 |
| 45.5 | 4.854 | 0.206 | 0.189 | BZ__45_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 864.5 | 466.375 | 398.125 | 4.901 | -0.278 | 4.195 | -0.069 | 10.139 | 5.093 | 4.037 | 3.128 |
| 46.0 | 3.901 | 0.183 | 0.697 | BZ__46_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 874.0 | 471.500 | 402.500 | 4.076 | 0.225 | 4.482 | 0.070 | 7.954 | 5.705 | 3.202 | 3.242 |
| 46.5 | 4.080 | 0.236 | 0.684 | BZ__46_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 883.5 | 476.625 | 406.875 | 4.251 | 0.208 | 5.548 | 0.062 | 8.277 | 7.004 | 3.368 | 3.283 |
| 47.0 | 4.199 | 0.195 | 0.450 | BZ__47_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 893.0 | 481.750 | 411.250 | 4.312 | -0.031 | 4.515 | -0.009 | 8.313 | 5.694 | 3.419 | 3.226 |
| 47.5 | 4.333 | 0.198 | 0.348 | BZ__47_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 902.5 | 486.875 | 415.625 | 4.420 | -0.138 | 4.475 | -0.039 | 8.463 | 5.623 | 3.518 | 3.216 |
| 48.0 | 4.478 | 0.263 | 1.248 | BZ__48_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 912.0 | 492.000 | 420.000 | 4.790 | 0.756 | 5.486 | 0.195 | 9.233 | 6.776 | 3.878 | 3.237 |
| 48.5 | 4.854 | 0.256 | 2.002 | BZ__48_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 921.5 | 497.125 | 424.375 | 5.355 | 1.505 | 4.778 | 0.340 | 10.446 | 5.771 | 4.433 | 3.152 |
| 49.0 | 5.238 | 0.210 | 2.240 | BZ__49_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 931.0 | 502.250 | 428.750 | 5.798 | 1.738 | 3.630 | 0.357 | 11.351 | 4.324 | 4.867 | 3.046 |
| 49.5 | 4.987 | 0.199 | 2.080 | BZ__49_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 940.5 | 507.375 | 433.125 | 5.507 | 1.572 | 3.615 | 0.344 | 10.543 | 4.359 | 4.566 | 3.073 |
| 50.0 | 4.736 | 0.188 | 1.919 | BZ__50_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 950.0 | 512.500 | 437.500 | 5.216 | 1.406 | 3.598 | 0.330 | 9.751 | 4.400 | 4.266 | 3.103 |
| 50.5 | 8.496 | 0.234 | 0.502 | BZ__50_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 959.5 | 517.625 | 441.875 | 8.621 | -0.016 | 2.709 | -0.002 | 17.339 | 3.048 | 7.662 | 2.807 |
| 51.0 | 3.739 | 0.102 | 2.134 | BZ__51_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 969.0 | 522.750 | 446.250 | 4.272 | 1.611 | 2.386 | 0.488 | 7.403 | 3.086 | 3.303 | 3.112 |
| 51.5 | 4.661 | 0.199 | 0.870 | BZ__51_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 978.5 | 527.875 | 450.625 | 4.878 | 0.342 | 4.087 | 0.088 | 8.654 | 5.113 | 3.900 | 3.184 |
| 52.0 | 11.025 | 0.235 | -0.570 | BZ__52_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 988.0 | 533.000 | 455.000 | 10.882 | -1.103 | 2.161 | -0.112 | 21.746 | 2.377 | 9.894 | 2.664 |
| 52.5 | 14.213 | 0.437 | -0.415 | BZ__52_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 997.5 | 538.125 | 459.375 | 14.109 | -0.954 | 3.101 | -0.073 | 28.542 | 3.337 | 13.112 | 2.664 |
| 54.0 | 11.391 | 0.420 | 0.123 | BZ__54_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 1026.0 | 553.500 | 472.500 | 11.422 | -0.430 | 3.675 | -0.041 | 22.002 | 4.038 | 10.396 | 2.804 |
| 57.5 | 5.914 | 0.269 | -0.017 | BZ__57_5 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 1092.5 | 589.375 | 503.125 | 5.910 | -0.606 | 4.559 | -0.126 | 9.575 | 5.593 | 4.817 | 3.173 |
| 58.0 | 10.613 | 0.352 | -0.069 | BZ__58_0 | BZ | NA | NA | NA | NA | NA | 0.75 | 1 | 19 | 1 | 1102.0 | 594.500 | 507.500 | 10.596 | -0.664 | 3.326 | -0.070 | 18.707 | 3.712 | 9.494 | 2.834 |
An additional file, which classifies the interdistance between each location pair, is provided . This file can be used to explore spatial correlation between locations.
kable(top_n(interdistance, 100),
digits = 3,
caption = "Interdistance data.",
align = "r") %>%
kable_styling(full_width = T, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6") %>%
scroll_box(height = "300px")| ID location 1 | ID location 2 | Interdistance class |
|---|---|---|
| AA | AD | 500m - 1500m |
| AA | AE | 500m - 1500m |
| AA | AL | 500m - 1500m |
| AA | AM | 500m - 1500m |
| AA | AN | 500m - 1500m |
| AA | AO | 500m - 1500m |
| AA | AP | 500m - 1500m |
| AA | AQ | 500m - 1500m |
| AB | AD | 500m - 1500m |
| AB | AE | 500m - 1500m |
| AB | AL | 500m - 1500m |
| AB | AM | 500m - 1500m |
| AB | AN | 500m - 1500m |
| AB | AO | 500m - 1500m |
| AB | AP | 500m - 1500m |
| AB | AQ | 500m - 1500m |
| AC | AD | 500m - 1500m |
| AC | AE | 500m - 1500m |
| AC | AL | 500m - 1500m |
| AC | AM | 500m - 1500m |
| AC | AN | 500m - 1500m |
| AC | AO | 500m - 1500m |
| AC | AP | 500m - 1500m |
| AC | AQ | 500m - 1500m |
| AD | AA | 500m - 1500m |
| AD | AB | 500m - 1500m |
| AD | AC | 500m - 1500m |
| AD | AF | 500m - 1500m |
| AD | AG | 500m - 1500m |
| AD | AH | 500m - 1500m |
| AD | AL | 500m - 1500m |
| AD | AM | 500m - 1500m |
| AD | AN | 500m - 1500m |
| AD | AO | 500m - 1500m |
| AD | AP | 500m - 1500m |
| AD | AQ | 500m - 1500m |
| AD | AR | 500m - 1500m |
| AD | AS | 500m - 1500m |
| AD | AT | 500m - 1500m |
| AE | AA | 500m - 1500m |
| AE | AB | 500m - 1500m |
| AE | AC | 500m - 1500m |
| AE | AF | 500m - 1500m |
| AE | AG | 500m - 1500m |
| AE | AH | 500m - 1500m |
| AE | AL | 500m - 1500m |
| AE | AM | 500m - 1500m |
| AE | AN | 500m - 1500m |
| AE | AO | 500m - 1500m |
| AE | AP | 500m - 1500m |
| AE | AQ | 500m - 1500m |
| AE | AR | 500m - 1500m |
| AE | AS | 500m - 1500m |
| AE | AT | 500m - 1500m |
| AF | AD | 500m - 1500m |
| AF | AE | 500m - 1500m |
| AF | AI | 500m - 1500m |
| AF | AJ | 500m - 1500m |
| AF | AK | 500m - 1500m |
| AF | AO | 500m - 1500m |
| AF | AP | 500m - 1500m |
| AF | AQ | 500m - 1500m |
| AF | AR | 500m - 1500m |
| AF | AS | 500m - 1500m |
| AF | AT | 500m - 1500m |
| AF | AU | 500m - 1500m |
| AF | AV | 500m - 1500m |
| AF | AW | 500m - 1500m |
| AG | AD | 500m - 1500m |
| AG | AE | 500m - 1500m |
| AG | AI | 500m - 1500m |
| AG | AJ | 500m - 1500m |
| AG | AK | 500m - 1500m |
| AG | AO | 500m - 1500m |
| AG | AP | 500m - 1500m |
| AG | AQ | 500m - 1500m |
| AG | AR | 500m - 1500m |
| AG | AS | 500m - 1500m |
| AG | AT | 500m - 1500m |
| AG | AU | 500m - 1500m |
| AG | AV | 500m - 1500m |
| AG | AW | 500m - 1500m |
| AH | AD | 500m - 1500m |
| AH | AE | 500m - 1500m |
| AH | AI | 500m - 1500m |
| AH | AJ | 500m - 1500m |
| AH | AK | 500m - 1500m |
| AH | AO | 500m - 1500m |
| AH | AP | 500m - 1500m |
| AH | AQ | 500m - 1500m |
| AH | AR | 500m - 1500m |
| AH | AS | 500m - 1500m |
| AH | AT | 500m - 1500m |
| AH | AU | 500m - 1500m |
| AH | AV | 500m - 1500m |
| AH | AW | 500m - 1500m |
| AI | AF | 500m - 1500m |
| AI | AG | 500m - 1500m |
| AI | AH | 500m - 1500m |
| AI | AR | 500m - 1500m |
| AI | AS | 500m - 1500m |
| AI | AT | 500m - 1500m |
| AI | AU | 500m - 1500m |
| AI | AV | 500m - 1500m |
| AI | AW | 500m - 1500m |
| AJ | AF | 500m - 1500m |
| AJ | AG | 500m - 1500m |
| AJ | AH | 500m - 1500m |
| AJ | AR | 500m - 1500m |
| AJ | AS | 500m - 1500m |
| AJ | AT | 500m - 1500m |
| AJ | AU | 500m - 1500m |
| AJ | AV | 500m - 1500m |
| AJ | AW | 500m - 1500m |
| AK | AF | 500m - 1500m |
| AK | AG | 500m - 1500m |
| AK | AH | 500m - 1500m |
| AK | AR | 500m - 1500m |
| AK | AS | 500m - 1500m |
| AK | AT | 500m - 1500m |
| AK | AU | 500m - 1500m |
| AK | AV | 500m - 1500m |
| AK | AW | 500m - 1500m |
| AL | AA | 500m - 1500m |
| AL | AB | 500m - 1500m |
| AL | AC | 500m - 1500m |
| AL | AD | 500m - 1500m |
| AL | AE | 500m - 1500m |
| AL | AO | 500m - 1500m |
| AL | AP | 500m - 1500m |
| AL | AQ | 500m - 1500m |
| AL | AX | 500m - 1500m |
| AL | AY | 500m - 1500m |
| AL | AZ | 500m - 1500m |
| AL | BA | 500m - 1500m |
| AL | BB | 500m - 1500m |
| AL | BC | 500m - 1500m |
| AM | AA | 500m - 1500m |
| AM | AB | 500m - 1500m |
| AM | AC | 500m - 1500m |
| AM | AD | 500m - 1500m |
| AM | AE | 500m - 1500m |
| AM | AO | 500m - 1500m |
| AM | AP | 500m - 1500m |
| AM | AQ | 500m - 1500m |
| AM | AX | 500m - 1500m |
| AM | AY | 500m - 1500m |
| AM | AZ | 500m - 1500m |
| AM | BA | 500m - 1500m |
| AM | BB | 500m - 1500m |
| AM | BC | 500m - 1500m |
| AN | AA | 500m - 1500m |
| AN | AB | 500m - 1500m |
| AN | AC | 500m - 1500m |
| AN | AD | 500m - 1500m |
| AN | AE | 500m - 1500m |
| AN | AO | 500m - 1500m |
| AN | AP | 500m - 1500m |
| AN | AQ | 500m - 1500m |
| AN | AX | 500m - 1500m |
| AN | AY | 500m - 1500m |
| AN | AZ | 500m - 1500m |
| AN | BA | 500m - 1500m |
| AN | BB | 500m - 1500m |
| AN | BC | 500m - 1500m |
| AO | AA | 500m - 1500m |
| AO | AB | 500m - 1500m |
| AO | AC | 500m - 1500m |
| AO | AD | 500m - 1500m |
| AO | AE | 500m - 1500m |
| AO | AF | 500m - 1500m |
| AO | AG | 500m - 1500m |
| AO | AH | 500m - 1500m |
| AO | AL | 500m - 1500m |
| AO | AM | 500m - 1500m |
| AO | AN | 500m - 1500m |
| AO | AR | 500m - 1500m |
| AO | AS | 500m - 1500m |
| AO | AT | 500m - 1500m |
| AO | AX | 500m - 1500m |
| AO | AY | 500m - 1500m |
| AO | AZ | 500m - 1500m |
| AO | BA | 500m - 1500m |
| AO | BB | 500m - 1500m |
| AO | BC | 500m - 1500m |
| AO | BD | 500m - 1500m |
| AO | BE | 500m - 1500m |
| AO | BF | 500m - 1500m |
| AP | AA | 500m - 1500m |
| AP | AB | 500m - 1500m |
| AP | AC | 500m - 1500m |
| AP | AD | 500m - 1500m |
| AP | AE | 500m - 1500m |
| AP | AF | 500m - 1500m |
| AP | AG | 500m - 1500m |
| AP | AH | 500m - 1500m |
| AP | AL | 500m - 1500m |
| AP | AM | 500m - 1500m |
| AP | AN | 500m - 1500m |
| AP | AR | 500m - 1500m |
| AP | AS | 500m - 1500m |
| AP | AT | 500m - 1500m |
| AP | AX | 500m - 1500m |
| AP | AY | 500m - 1500m |
| AP | AZ | 500m - 1500m |
| AP | BA | 500m - 1500m |
| AP | BB | 500m - 1500m |
| AP | BC | 500m - 1500m |
| AP | BD | 500m - 1500m |
| AP | BE | 500m - 1500m |
| AP | BF | 500m - 1500m |
| AQ | AA | 500m - 1500m |
| AQ | AB | 500m - 1500m |
| AQ | AC | 500m - 1500m |
| AQ | AD | 500m - 1500m |
| AQ | AE | 500m - 1500m |
| AQ | AF | 500m - 1500m |
| AQ | AG | 500m - 1500m |
| AQ | AH | 500m - 1500m |
| AQ | AL | 500m - 1500m |
| AQ | AM | 500m - 1500m |
| AQ | AN | 500m - 1500m |
| AQ | AR | 500m - 1500m |
| AQ | AS | 500m - 1500m |
| AQ | AT | 500m - 1500m |
| AQ | AX | 500m - 1500m |
| AQ | AY | 500m - 1500m |
| AQ | AZ | 500m - 1500m |
| AQ | BA | 500m - 1500m |
| AQ | BB | 500m - 1500m |
| AQ | BC | 500m - 1500m |
| AQ | BD | 500m - 1500m |
| AQ | BE | 500m - 1500m |
| AQ | BF | 500m - 1500m |
| AR | AD | 500m - 1500m |
| AR | AE | 500m - 1500m |
| AR | AF | 500m - 1500m |
| AR | AG | 500m - 1500m |
| AR | AH | 500m - 1500m |
| AR | AI | 500m - 1500m |
| AR | AJ | 500m - 1500m |
| AR | AK | 500m - 1500m |
| AR | AO | 500m - 1500m |
| AR | AP | 500m - 1500m |
| AR | AQ | 500m - 1500m |
| AR | AU | 500m - 1500m |
| AR | AV | 500m - 1500m |
| AR | AW | 500m - 1500m |
| AR | BA | 500m - 1500m |
| AR | BB | 500m - 1500m |
| AR | BC | 500m - 1500m |
| AR | BD | 500m - 1500m |
| AR | BE | 500m - 1500m |
| AR | BF | 500m - 1500m |
| AR | BG | 500m - 1500m |
| AR | BH | 500m - 1500m |
| AR | BI | 500m - 1500m |
| AS | AD | 500m - 1500m |
| AS | AE | 500m - 1500m |
| AS | AF | 500m - 1500m |
| AS | AG | 500m - 1500m |
| AS | AH | 500m - 1500m |
| AS | AI | 500m - 1500m |
| AS | AJ | 500m - 1500m |
| AS | AK | 500m - 1500m |
| AS | AO | 500m - 1500m |
| AS | AP | 500m - 1500m |
| AS | AQ | 500m - 1500m |
| AS | AU | 500m - 1500m |
| AS | AV | 500m - 1500m |
| AS | AW | 500m - 1500m |
| AS | BA | 500m - 1500m |
| AS | BB | 500m - 1500m |
| AS | BC | 500m - 1500m |
| AS | BD | 500m - 1500m |
| AS | BE | 500m - 1500m |
| AS | BF | 500m - 1500m |
| AS | BG | 500m - 1500m |
| AS | BH | 500m - 1500m |
| AS | BI | 500m - 1500m |
| AT | AD | 500m - 1500m |
| AT | AE | 500m - 1500m |
| AT | AF | 500m - 1500m |
| AT | AG | 500m - 1500m |
| AT | AH | 500m - 1500m |
| AT | AI | 500m - 1500m |
| AT | AJ | 500m - 1500m |
| AT | AK | 500m - 1500m |
| AT | AO | 500m - 1500m |
| AT | AP | 500m - 1500m |
| AT | AQ | 500m - 1500m |
| AT | AU | 500m - 1500m |
| AT | AV | 500m - 1500m |
| AT | AW | 500m - 1500m |
| AT | BA | 500m - 1500m |
| AT | BB | 500m - 1500m |
| AT | BC | 500m - 1500m |
| AT | BD | 500m - 1500m |
| AT | BE | 500m - 1500m |
| AT | BF | 500m - 1500m |
| AT | BG | 500m - 1500m |
| AT | BH | 500m - 1500m |
| AT | BI | 500m - 1500m |
| AU | AF | 500m - 1500m |
| AU | AG | 500m - 1500m |
| AU | AH | 500m - 1500m |
| AU | AI | 500m - 1500m |
| AU | AJ | 500m - 1500m |
| AU | AK | 500m - 1500m |
| AU | AR | 500m - 1500m |
| AU | AS | 500m - 1500m |
| AU | AT | 500m - 1500m |
| AU | BD | 500m - 1500m |
| AU | BE | 500m - 1500m |
| AU | BF | 500m - 1500m |
| AU | BG | 500m - 1500m |
| AU | BH | 500m - 1500m |
| AU | BI | 500m - 1500m |
| AV | AF | 500m - 1500m |
| AV | AG | 500m - 1500m |
| AV | AH | 500m - 1500m |
| AV | AI | 500m - 1500m |
| AV | AJ | 500m - 1500m |
| AV | AK | 500m - 1500m |
| AV | AR | 500m - 1500m |
| AV | AS | 500m - 1500m |
| AV | AT | 500m - 1500m |
| AV | BD | 500m - 1500m |
| AV | BE | 500m - 1500m |
| AV | BF | 500m - 1500m |
| AV | BG | 500m - 1500m |
| AV | BH | 500m - 1500m |
| AV | BI | 500m - 1500m |
| AW | AF | 500m - 1500m |
| AW | AG | 500m - 1500m |
| AW | AH | 500m - 1500m |
| AW | AI | 500m - 1500m |
| AW | AJ | 500m - 1500m |
| AW | AK | 500m - 1500m |
| AW | AR | 500m - 1500m |
| AW | AS | 500m - 1500m |
| AW | AT | 500m - 1500m |
| AW | BD | 500m - 1500m |
| AW | BE | 500m - 1500m |
| AW | BF | 500m - 1500m |
| AW | BG | 500m - 1500m |
| AW | BH | 500m - 1500m |
| AW | BI | 500m - 1500m |
| AX | AL | 500m - 1500m |
| AX | AM | 500m - 1500m |
| AX | AN | 500m - 1500m |
| AX | AO | 500m - 1500m |
| AX | AP | 500m - 1500m |
| AX | AQ | 500m - 1500m |
| AX | BA | 500m - 1500m |
| AX | BB | 500m - 1500m |
| AX | BC | 500m - 1500m |
| AX | BR | 500m - 1500m |
| AX | BS | 500m - 1500m |
| AX | BT | 500m - 1500m |
| AX | BU | 500m - 1500m |
| AX | BV | 500m - 1500m |
| AX | BW | 500m - 1500m |
| AY | AL | 500m - 1500m |
| AY | AM | 500m - 1500m |
| AY | AN | 500m - 1500m |
| AY | AO | 500m - 1500m |
| AY | AP | 500m - 1500m |
| AY | AQ | 500m - 1500m |
| AY | BA | 500m - 1500m |
| AY | BB | 500m - 1500m |
| AY | BC | 500m - 1500m |
| AY | BR | 500m - 1500m |
| AY | BS | 500m - 1500m |
| AY | BT | 500m - 1500m |
| AY | BU | 500m - 1500m |
| AY | BV | 500m - 1500m |
| AY | BW | 500m - 1500m |
| AZ | AL | 500m - 1500m |
| AZ | AM | 500m - 1500m |
| AZ | AN | 500m - 1500m |
| AZ | AO | 500m - 1500m |
| AZ | AP | 500m - 1500m |
| AZ | AQ | 500m - 1500m |
| AZ | BA | 500m - 1500m |
| AZ | BB | 500m - 1500m |
| AZ | BC | 500m - 1500m |
| AZ | BR | 500m - 1500m |
| AZ | BS | 500m - 1500m |
| AZ | BT | 500m - 1500m |
| AZ | BU | 500m - 1500m |
| AZ | BV | 500m - 1500m |
| AZ | BW | 500m - 1500m |
| BA | AL | 500m - 1500m |
| BA | AM | 500m - 1500m |
| BA | AN | 500m - 1500m |
| BA | AO | 500m - 1500m |
| BA | AP | 500m - 1500m |
| BA | AQ | 500m - 1500m |
| BA | AR | 500m - 1500m |
| BA | AS | 500m - 1500m |
| BA | AT | 500m - 1500m |
| BA | AX | 500m - 1500m |
| BA | AY | 500m - 1500m |
| BA | AZ | 500m - 1500m |
| BA | BD | 500m - 1500m |
| BA | BE | 500m - 1500m |
| BA | BF | 500m - 1500m |
| BA | BR | 500m - 1500m |
| BA | BS | 500m - 1500m |
| BA | BT | 500m - 1500m |
| BA | BU | 500m - 1500m |
| BA | BV | 500m - 1500m |
| BA | BW | 500m - 1500m |
| BA | BX | 500m - 1500m |
| BA | BY | 500m - 1500m |
| BA | BZ | 500m - 1500m |
| BB | AL | 500m - 1500m |
| BB | AM | 500m - 1500m |
| BB | AN | 500m - 1500m |
| BB | AO | 500m - 1500m |
| BB | AP | 500m - 1500m |
| BB | AQ | 500m - 1500m |
| BB | AR | 500m - 1500m |
| BB | AS | 500m - 1500m |
| BB | AT | 500m - 1500m |
| BB | AX | 500m - 1500m |
| BB | AY | 500m - 1500m |
| BB | AZ | 500m - 1500m |
| BB | BD | 500m - 1500m |
| BB | BE | 500m - 1500m |
| BB | BF | 500m - 1500m |
| BB | BR | 500m - 1500m |
| BB | BS | 500m - 1500m |
| BB | BT | 500m - 1500m |
| BB | BU | 500m - 1500m |
| BB | BV | 500m - 1500m |
| BB | BW | 500m - 1500m |
| BB | BX | 500m - 1500m |
| BB | BY | 500m - 1500m |
| BB | BZ | 500m - 1500m |
| BC | AL | 500m - 1500m |
| BC | AM | 500m - 1500m |
| BC | AN | 500m - 1500m |
| BC | AO | 500m - 1500m |
| BC | AP | 500m - 1500m |
| BC | AQ | 500m - 1500m |
| BC | AR | 500m - 1500m |
| BC | AS | 500m - 1500m |
| BC | AT | 500m - 1500m |
| BC | AX | 500m - 1500m |
| BC | AY | 500m - 1500m |
| BC | AZ | 500m - 1500m |
| BC | BD | 500m - 1500m |
| BC | BE | 500m - 1500m |
| BC | BF | 500m - 1500m |
| BC | BR | 500m - 1500m |
| BC | BS | 500m - 1500m |
| BC | BT | 500m - 1500m |
| BC | BU | 500m - 1500m |
| BC | BV | 500m - 1500m |
| BC | BW | 500m - 1500m |
| BC | BX | 500m - 1500m |
| BC | BY | 500m - 1500m |
| BC | BZ | 500m - 1500m |
| BD | AO | 500m - 1500m |
| BD | AP | 500m - 1500m |
| BD | AQ | 500m - 1500m |
| BD | AR | 500m - 1500m |
| BD | AS | 500m - 1500m |
| BD | AT | 500m - 1500m |
| BD | AU | 500m - 1500m |
| BD | AV | 500m - 1500m |
| BD | AW | 500m - 1500m |
| BD | BA | 500m - 1500m |
| BD | BB | 500m - 1500m |
| BD | BC | 500m - 1500m |
| BD | BG | 500m - 1500m |
| BD | BH | 500m - 1500m |
| BD | BI | 500m - 1500m |
| BD | BU | 500m - 1500m |
| BD | BV | 500m - 1500m |
| BD | BW | 500m - 1500m |
| BD | BX | 500m - 1500m |
| BD | BY | 500m - 1500m |
| BD | BZ | 500m - 1500m |
| BD | CA | 500m - 1500m |
| BD | CB | 500m - 1500m |
| BD | CC | 500m - 1500m |
| BE | AO | 500m - 1500m |
| BE | AP | 500m - 1500m |
| BE | AQ | 500m - 1500m |
| BE | AR | 500m - 1500m |
| BE | AS | 500m - 1500m |
| BE | AT | 500m - 1500m |
| BE | AU | 500m - 1500m |
| BE | AV | 500m - 1500m |
| BE | AW | 500m - 1500m |
| BE | BA | 500m - 1500m |
| BE | BB | 500m - 1500m |
| BE | BC | 500m - 1500m |
| BE | BG | 500m - 1500m |
| BE | BH | 500m - 1500m |
| BE | BI | 500m - 1500m |
| BE | BU | 500m - 1500m |
| BE | BV | 500m - 1500m |
| BE | BW | 500m - 1500m |
| BE | BX | 500m - 1500m |
| BE | BY | 500m - 1500m |
| BE | BZ | 500m - 1500m |
| BE | CA | 500m - 1500m |
| BE | CB | 500m - 1500m |
| BE | CC | 500m - 1500m |
| BF | AO | 500m - 1500m |
| BF | AP | 500m - 1500m |
| BF | AQ | 500m - 1500m |
| BF | AR | 500m - 1500m |
| BF | AS | 500m - 1500m |
| BF | AT | 500m - 1500m |
| BF | AU | 500m - 1500m |
| BF | AV | 500m - 1500m |
| BF | AW | 500m - 1500m |
| BF | BA | 500m - 1500m |
| BF | BB | 500m - 1500m |
| BF | BC | 500m - 1500m |
| BF | BG | 500m - 1500m |
| BF | BH | 500m - 1500m |
| BF | BI | 500m - 1500m |
| BF | BU | 500m - 1500m |
| BF | BV | 500m - 1500m |
| BF | BW | 500m - 1500m |
| BF | BX | 500m - 1500m |
| BF | BY | 500m - 1500m |
| BF | BZ | 500m - 1500m |
| BF | CA | 500m - 1500m |
| BF | CB | 500m - 1500m |
| BF | CC | 500m - 1500m |
| BG | AR | 500m - 1500m |
| BG | AS | 500m - 1500m |
| BG | AT | 500m - 1500m |
| BG | AU | 500m - 1500m |
| BG | AV | 500m - 1500m |
| BG | AW | 500m - 1500m |
| BG | BD | 500m - 1500m |
| BG | BE | 500m - 1500m |
| BG | BF | 500m - 1500m |
| BG | BX | 500m - 1500m |
| BG | BY | 500m - 1500m |
| BG | BZ | 500m - 1500m |
| BG | CA | 500m - 1500m |
| BG | CB | 500m - 1500m |
| BG | CC | 500m - 1500m |
| BH | AR | 500m - 1500m |
| BH | AS | 500m - 1500m |
| BH | AT | 500m - 1500m |
| BH | AU | 500m - 1500m |
| BH | AV | 500m - 1500m |
| BH | AW | 500m - 1500m |
| BH | BD | 500m - 1500m |
| BH | BE | 500m - 1500m |
| BH | BF | 500m - 1500m |
| BH | BX | 500m - 1500m |
| BH | BY | 500m - 1500m |
| BH | BZ | 500m - 1500m |
| BH | CA | 500m - 1500m |
| BH | CB | 500m - 1500m |
| BH | CC | 500m - 1500m |
| BI | AR | 500m - 1500m |
| BI | AS | 500m - 1500m |
| BI | AT | 500m - 1500m |
| BI | AU | 500m - 1500m |
| BI | AV | 500m - 1500m |
| BI | AW | 500m - 1500m |
| BI | BD | 500m - 1500m |
| BI | BE | 500m - 1500m |
| BI | BF | 500m - 1500m |
| BI | BX | 500m - 1500m |
| BI | BY | 500m - 1500m |
| BI | BZ | 500m - 1500m |
| BI | CA | 500m - 1500m |
| BI | CB | 500m - 1500m |
| BI | CC | 500m - 1500m |
| BJ | BL | 500m - 1500m |
| BJ | BM | 500m - 1500m |
| BJ | BN | 500m - 1500m |
| BJ | CG | 500m - 1500m |
| BJ | CH | 500m - 1500m |
| BJ | CI | 500m - 1500m |
| BJ | CJ | 500m - 1500m |
| BJ | CK | 500m - 1500m |
| BJ | CL | 500m - 1500m |
| BJ | CM | 500m - 1500m |
| BJ | CN | 500m - 1500m |
| BJ | CO | 500m - 1500m |
| BK | BL | 500m - 1500m |
| BK | BM | 500m - 1500m |
| BK | BN | 500m - 1500m |
| BK | CG | 500m - 1500m |
| BK | CH | 500m - 1500m |
| BK | CI | 500m - 1500m |
| BK | CJ | 500m - 1500m |
| BK | CK | 500m - 1500m |
| BK | CL | 500m - 1500m |
| BK | CM | 500m - 1500m |
| BK | CN | 500m - 1500m |
| BK | CO | 500m - 1500m |
| BL | BJ | 500m - 1500m |
| BL | BK | 500m - 1500m |
| BL | BO | 500m - 1500m |
| BL | BP | 500m - 1500m |
| BL | BQ | 500m - 1500m |
| BL | CJ | 500m - 1500m |
| BL | CK | 500m - 1500m |
| BL | CL | 500m - 1500m |
| BL | CM | 500m - 1500m |
| BL | CN | 500m - 1500m |
| BL | CO | 500m - 1500m |
| BL | CP | 500m - 1500m |
| BL | CQ | 500m - 1500m |
| BL | CR | 500m - 1500m |
| BM | BJ | 500m - 1500m |
| BM | BK | 500m - 1500m |
| BM | BO | 500m - 1500m |
| BM | BP | 500m - 1500m |
| BM | BQ | 500m - 1500m |
| BM | CJ | 500m - 1500m |
| BM | CK | 500m - 1500m |
| BM | CL | 500m - 1500m |
| BM | CM | 500m - 1500m |
| BM | CN | 500m - 1500m |
| BM | CO | 500m - 1500m |
| BM | CP | 500m - 1500m |
| BM | CQ | 500m - 1500m |
| BM | CR | 500m - 1500m |
| BN | BJ | 500m - 1500m |
| BN | BK | 500m - 1500m |
| BN | BO | 500m - 1500m |
| BN | BP | 500m - 1500m |
| BN | BQ | 500m - 1500m |
| BN | CJ | 500m - 1500m |
| BN | CK | 500m - 1500m |
| BN | CL | 500m - 1500m |
| BN | CM | 500m - 1500m |
| BN | CN | 500m - 1500m |
| BN | CO | 500m - 1500m |
| BN | CP | 500m - 1500m |
| BN | CQ | 500m - 1500m |
| BN | CR | 500m - 1500m |
| BO | BL | 500m - 1500m |
| BO | BM | 500m - 1500m |
| BO | BN | 500m - 1500m |
| BO | CM | 500m - 1500m |
| BO | CN | 500m - 1500m |
| BO | CO | 500m - 1500m |
| BO | CP | 500m - 1500m |
| BO | CQ | 500m - 1500m |
| BO | CR | 500m - 1500m |
| BP | BL | 500m - 1500m |
| BP | BM | 500m - 1500m |
| BP | BN | 500m - 1500m |
| BP | CM | 500m - 1500m |
| BP | CN | 500m - 1500m |
| BP | CO | 500m - 1500m |
| BP | CP | 500m - 1500m |
| BP | CQ | 500m - 1500m |
| BP | CR | 500m - 1500m |
| BQ | BL | 500m - 1500m |
| BQ | BM | 500m - 1500m |
| BQ | BN | 500m - 1500m |
| BQ | CM | 500m - 1500m |
| BQ | CN | 500m - 1500m |
| BQ | CO | 500m - 1500m |
| BQ | CP | 500m - 1500m |
| BQ | CQ | 500m - 1500m |
| BQ | CR | 500m - 1500m |
| BR | AX | 500m - 1500m |
| BR | AY | 500m - 1500m |
| BR | AZ | 500m - 1500m |
| BR | BA | 500m - 1500m |
| BR | BB | 500m - 1500m |
| BR | BC | 500m - 1500m |
| BR | BU | 500m - 1500m |
| BR | BV | 500m - 1500m |
| BR | BW | 500m - 1500m |
| BR | CS | 500m - 1500m |
| BR | CT | 500m - 1500m |
| BR | CU | 500m - 1500m |
| BS | AX | 500m - 1500m |
| BS | AY | 500m - 1500m |
| BS | AZ | 500m - 1500m |
| BS | BA | 500m - 1500m |
| BS | BB | 500m - 1500m |
| BS | BC | 500m - 1500m |
| BS | BU | 500m - 1500m |
| BS | BV | 500m - 1500m |
| BS | BW | 500m - 1500m |
| BS | CS | 500m - 1500m |
| BS | CT | 500m - 1500m |
| BS | CU | 500m - 1500m |
| BT | AX | 500m - 1500m |
| BT | AY | 500m - 1500m |
| BT | AZ | 500m - 1500m |
| BT | BA | 500m - 1500m |
| BT | BB | 500m - 1500m |
| BT | BC | 500m - 1500m |
| BT | BU | 500m - 1500m |
| BT | BV | 500m - 1500m |
| BT | BW | 500m - 1500m |
| BT | CS | 500m - 1500m |
| BT | CT | 500m - 1500m |
| BT | CU | 500m - 1500m |
| BU | AX | 500m - 1500m |
| BU | AY | 500m - 1500m |
| BU | AZ | 500m - 1500m |
| BU | BA | 500m - 1500m |
| BU | BB | 500m - 1500m |
| BU | BC | 500m - 1500m |
| BU | BD | 500m - 1500m |
| BU | BE | 500m - 1500m |
| BU | BF | 500m - 1500m |
| BU | BR | 500m - 1500m |
| BU | BS | 500m - 1500m |
| BU | BT | 500m - 1500m |
| BU | BX | 500m - 1500m |
| BU | BY | 500m - 1500m |
| BU | BZ | 500m - 1500m |
| BU | CS | 500m - 1500m |
| BU | CT | 500m - 1500m |
| BU | CU | 500m - 1500m |
| BU | CV | 500m - 1500m |
| BU | CW | 500m - 1500m |
| BU | CX | 500m - 1500m |
| BV | AX | 500m - 1500m |
| BV | AY | 500m - 1500m |
| BV | AZ | 500m - 1500m |
| BV | BA | 500m - 1500m |
| BV | BB | 500m - 1500m |
| BV | BC | 500m - 1500m |
| BV | BD | 500m - 1500m |
| BV | BE | 500m - 1500m |
| BV | BF | 500m - 1500m |
| BV | BR | 500m - 1500m |
| BV | BS | 500m - 1500m |
| BV | BT | 500m - 1500m |
| BV | BX | 500m - 1500m |
| BV | BY | 500m - 1500m |
| BV | BZ | 500m - 1500m |
| BV | CS | 500m - 1500m |
| BV | CT | 500m - 1500m |
| BV | CU | 500m - 1500m |
| BV | CV | 500m - 1500m |
| BV | CW | 500m - 1500m |
| BV | CX | 500m - 1500m |
| BW | AX | 500m - 1500m |
| BW | AY | 500m - 1500m |
| BW | AZ | 500m - 1500m |
| BW | BA | 500m - 1500m |
| BW | BB | 500m - 1500m |
| BW | BC | 500m - 1500m |
| BW | BD | 500m - 1500m |
| BW | BE | 500m - 1500m |
| BW | BF | 500m - 1500m |
| BW | BR | 500m - 1500m |
| BW | BS | 500m - 1500m |
| BW | BT | 500m - 1500m |
| BW | BX | 500m - 1500m |
| BW | BY | 500m - 1500m |
| BW | BZ | 500m - 1500m |
| BW | CS | 500m - 1500m |
| BW | CT | 500m - 1500m |
| BW | CU | 500m - 1500m |
| BW | CV | 500m - 1500m |
| BW | CW | 500m - 1500m |
| BW | CX | 500m - 1500m |
| BX | BA | 500m - 1500m |
| BX | BB | 500m - 1500m |
| BX | BC | 500m - 1500m |
| BX | BD | 500m - 1500m |
| BX | BE | 500m - 1500m |
| BX | BF | 500m - 1500m |
| BX | BG | 500m - 1500m |
| BX | BH | 500m - 1500m |
| BX | BI | 500m - 1500m |
| BX | BU | 500m - 1500m |
| BX | BV | 500m - 1500m |
| BX | BW | 500m - 1500m |
| BX | CA | 500m - 1500m |
| BX | CB | 500m - 1500m |
| BX | CC | 500m - 1500m |
| BX | CS | 500m - 1500m |
| BX | CT | 500m - 1500m |
| BX | CU | 500m - 1500m |
| BX | CV | 500m - 1500m |
| BX | CW | 500m - 1500m |
| BX | CX | 500m - 1500m |
| BX | CY | 500m - 1500m |
| BX | CZ | 500m - 1500m |
| BX | DA | 500m - 1500m |
| BY | BA | 500m - 1500m |
| BY | BB | 500m - 1500m |
| BY | BC | 500m - 1500m |
| BY | BD | 500m - 1500m |
| BY | BE | 500m - 1500m |
| BY | BF | 500m - 1500m |
| BY | BG | 500m - 1500m |
| BY | BH | 500m - 1500m |
| BY | BI | 500m - 1500m |
| BY | BU | 500m - 1500m |
| BY | BV | 500m - 1500m |
| BY | BW | 500m - 1500m |
| BY | CA | 500m - 1500m |
| BY | CB | 500m - 1500m |
| BY | CC | 500m - 1500m |
| BY | CS | 500m - 1500m |
| BY | CT | 500m - 1500m |
| BY | CU | 500m - 1500m |
| BY | CV | 500m - 1500m |
| BY | CW | 500m - 1500m |
| BY | CX | 500m - 1500m |
| BY | CY | 500m - 1500m |
| BY | CZ | 500m - 1500m |
| BY | DA | 500m - 1500m |
| BZ | BA | 500m - 1500m |
| BZ | BB | 500m - 1500m |
| BZ | BC | 500m - 1500m |
| BZ | BD | 500m - 1500m |
| BZ | BE | 500m - 1500m |
| BZ | BF | 500m - 1500m |
| BZ | BG | 500m - 1500m |
| BZ | BH | 500m - 1500m |
| BZ | BI | 500m - 1500m |
| BZ | BU | 500m - 1500m |
| BZ | BV | 500m - 1500m |
| BZ | BW | 500m - 1500m |
| BZ | CA | 500m - 1500m |
| BZ | CB | 500m - 1500m |
| BZ | CC | 500m - 1500m |
| BZ | CS | 500m - 1500m |
| BZ | CT | 500m - 1500m |
| BZ | CU | 500m - 1500m |
| BZ | CV | 500m - 1500m |
| BZ | CW | 500m - 1500m |
| BZ | CX | 500m - 1500m |
| BZ | CY | 500m - 1500m |
| BZ | CZ | 500m - 1500m |
| BZ | DA | 500m - 1500m |
| CA | BD | 500m - 1500m |
| CA | BE | 500m - 1500m |
| CA | BF | 500m - 1500m |
| CA | BG | 500m - 1500m |
| CA | BH | 500m - 1500m |
| CA | BI | 500m - 1500m |
| CA | BX | 500m - 1500m |
| CA | BY | 500m - 1500m |
| CA | BZ | 500m - 1500m |
| CA | CV | 500m - 1500m |
| CA | CW | 500m - 1500m |
| CA | CX | 500m - 1500m |
| CA | CY | 500m - 1500m |
| CA | CZ | 500m - 1500m |
| CA | DA | 500m - 1500m |
| CA | DB | 500m - 1500m |
| CA | DC | 500m - 1500m |
| CA | DD | 500m - 1500m |
| CB | BD | 500m - 1500m |
| CB | BE | 500m - 1500m |
| CB | BF | 500m - 1500m |
| CB | BG | 500m - 1500m |
| CB | BH | 500m - 1500m |
| CB | BI | 500m - 1500m |
| CB | BX | 500m - 1500m |
| CB | BY | 500m - 1500m |
| CB | BZ | 500m - 1500m |
| CB | CV | 500m - 1500m |
| CB | CW | 500m - 1500m |
| CB | CX | 500m - 1500m |
| CB | CY | 500m - 1500m |
| CB | CZ | 500m - 1500m |
| CB | DA | 500m - 1500m |
| CB | DB | 500m - 1500m |
| CB | DC | 500m - 1500m |
| CB | DD | 500m - 1500m |
| CC | BD | 500m - 1500m |
| CC | BE | 500m - 1500m |
| CC | BF | 500m - 1500m |
| CC | BG | 500m - 1500m |
| CC | BH | 500m - 1500m |
| CC | BI | 500m - 1500m |
| CC | BX | 500m - 1500m |
| CC | BY | 500m - 1500m |
| CC | BZ | 500m - 1500m |
| CC | CV | 500m - 1500m |
| CC | CW | 500m - 1500m |
| CC | CX | 500m - 1500m |
| CC | CY | 500m - 1500m |
| CC | CZ | 500m - 1500m |
| CC | DA | 500m - 1500m |
| CC | DB | 500m - 1500m |
| CC | DC | 500m - 1500m |
| CC | DD | 500m - 1500m |
| CD | CG | 500m - 1500m |
| CD | CH | 500m - 1500m |
| CD | CI | 500m - 1500m |
| CD | DH | 500m - 1500m |
| CD | DI | 500m - 1500m |
| CD | DJ | 500m - 1500m |
| CD | DK | 500m - 1500m |
| CD | DL | 500m - 1500m |
| CD | DM | 500m - 1500m |
| CE | CG | 500m - 1500m |
| CE | CH | 500m - 1500m |
| CE | CI | 500m - 1500m |
| CE | DH | 500m - 1500m |
| CE | DI | 500m - 1500m |
| CE | DJ | 500m - 1500m |
| CE | DK | 500m - 1500m |
| CE | DL | 500m - 1500m |
| CE | DM | 500m - 1500m |
| CF | CG | 500m - 1500m |
| CF | CH | 500m - 1500m |
| CF | CI | 500m - 1500m |
| CF | DH | 500m - 1500m |
| CF | DI | 500m - 1500m |
| CF | DJ | 500m - 1500m |
| CF | DK | 500m - 1500m |
| CF | DL | 500m - 1500m |
| CF | DM | 500m - 1500m |
| CG | BJ | 500m - 1500m |
| CG | BK | 500m - 1500m |
| CG | CD | 500m - 1500m |
| CG | CE | 500m - 1500m |
| CG | CF | 500m - 1500m |
| CG | CJ | 500m - 1500m |
| CG | CK | 500m - 1500m |
| CG | CL | 500m - 1500m |
| CG | DK | 500m - 1500m |
| CG | DL | 500m - 1500m |
| CG | DM | 500m - 1500m |
| CG | DN | 500m - 1500m |
| CG | DO | 500m - 1500m |
| CG | DP | 500m - 1500m |
| CH | BJ | 500m - 1500m |
| CH | BK | 500m - 1500m |
| CH | CD | 500m - 1500m |
| CH | CE | 500m - 1500m |
| CH | CF | 500m - 1500m |
| CH | CJ | 500m - 1500m |
| CH | CK | 500m - 1500m |
| CH | CL | 500m - 1500m |
| CH | DK | 500m - 1500m |
| CH | DL | 500m - 1500m |
| CH | DM | 500m - 1500m |
| CH | DN | 500m - 1500m |
| CH | DO | 500m - 1500m |
| CH | DP | 500m - 1500m |
| CI | BJ | 500m - 1500m |
| CI | BK | 500m - 1500m |
| CI | CD | 500m - 1500m |
| CI | CE | 500m - 1500m |
| CI | CF | 500m - 1500m |
| CI | CJ | 500m - 1500m |
| CI | CK | 500m - 1500m |
| CI | CL | 500m - 1500m |
| CI | DK | 500m - 1500m |
| CI | DL | 500m - 1500m |
| CI | DM | 500m - 1500m |
| CI | DN | 500m - 1500m |
| CI | DO | 500m - 1500m |
| CI | DP | 500m - 1500m |
| CJ | BJ | 500m - 1500m |
| CJ | BK | 500m - 1500m |
| CJ | BL | 500m - 1500m |
| CJ | BM | 500m - 1500m |
| CJ | BN | 500m - 1500m |
| CJ | CG | 500m - 1500m |
| CJ | CH | 500m - 1500m |
| CJ | CI | 500m - 1500m |
| CJ | CM | 500m - 1500m |
| CJ | CN | 500m - 1500m |
| CJ | CO | 500m - 1500m |
| CJ | DN | 500m - 1500m |
| CJ | DO | 500m - 1500m |
| CJ | DP | 500m - 1500m |
| CJ | DQ | 500m - 1500m |
| CJ | DR | 500m - 1500m |
| CJ | DS | 500m - 1500m |
| CK | BJ | 500m - 1500m |
| CK | BK | 500m - 1500m |
| CK | BL | 500m - 1500m |
| CK | BM | 500m - 1500m |
| CK | BN | 500m - 1500m |
| CK | CG | 500m - 1500m |
| CK | CH | 500m - 1500m |
| CK | CI | 500m - 1500m |
| CK | CM | 500m - 1500m |
| CK | CN | 500m - 1500m |
| CK | CO | 500m - 1500m |
| CK | DN | 500m - 1500m |
| CK | DO | 500m - 1500m |
| CK | DP | 500m - 1500m |
| CK | DQ | 500m - 1500m |
| CK | DR | 500m - 1500m |
| CK | DS | 500m - 1500m |
| CL | BJ | 500m - 1500m |
| CL | BK | 500m - 1500m |
| CL | BL | 500m - 1500m |
| CL | BM | 500m - 1500m |
| CL | BN | 500m - 1500m |
| CL | CG | 500m - 1500m |
| CL | CH | 500m - 1500m |
| CL | CI | 500m - 1500m |
| CL | CM | 500m - 1500m |
| CL | CN | 500m - 1500m |
| CL | CO | 500m - 1500m |
| CL | DN | 500m - 1500m |
| CL | DO | 500m - 1500m |
| CL | DP | 500m - 1500m |
| CL | DQ | 500m - 1500m |
| CL | DR | 500m - 1500m |
| CL | DS | 500m - 1500m |
| CM | BJ | 500m - 1500m |
| CM | BK | 500m - 1500m |
| CM | BL | 500m - 1500m |
| CM | BM | 500m - 1500m |
| CM | BN | 500m - 1500m |
| CM | BO | 500m - 1500m |
| CM | BP | 500m - 1500m |
| CM | BQ | 500m - 1500m |
| CM | CJ | 500m - 1500m |
| CM | CK | 500m - 1500m |
| CM | CL | 500m - 1500m |
| CM | CP | 500m - 1500m |
| CM | CQ | 500m - 1500m |
| CM | CR | 500m - 1500m |
| CM | DN | 500m - 1500m |
| CM | DO | 500m - 1500m |
| CM | DP | 500m - 1500m |
| CM | DQ | 500m - 1500m |
| CM | DR | 500m - 1500m |
| CM | DS | 500m - 1500m |
| CM | DT | 500m - 1500m |
| CM | DU | 500m - 1500m |
| CM | DV | 500m - 1500m |
| CN | BJ | 500m - 1500m |
| CN | BK | 500m - 1500m |
| CN | BL | 500m - 1500m |
| CN | BM | 500m - 1500m |
| CN | BN | 500m - 1500m |
| CN | BO | 500m - 1500m |
| CN | BP | 500m - 1500m |
| CN | BQ | 500m - 1500m |
| CN | CJ | 500m - 1500m |
| CN | CK | 500m - 1500m |
| CN | CL | 500m - 1500m |
| CN | CP | 500m - 1500m |
| CN | CQ | 500m - 1500m |
| CN | CR | 500m - 1500m |
| CN | DN | 500m - 1500m |
| CN | DO | 500m - 1500m |
| CN | DP | 500m - 1500m |
| CN | DQ | 500m - 1500m |
| CN | DR | 500m - 1500m |
| CN | DS | 500m - 1500m |
| CN | DT | 500m - 1500m |
| CN | DU | 500m - 1500m |
| CN | DV | 500m - 1500m |
| CO | BJ | 500m - 1500m |
| CO | BK | 500m - 1500m |
| CO | BL | 500m - 1500m |
| CO | BM | 500m - 1500m |
| CO | BN | 500m - 1500m |
| CO | BO | 500m - 1500m |
| CO | BP | 500m - 1500m |
| CO | BQ | 500m - 1500m |
| CO | CJ | 500m - 1500m |
| CO | CK | 500m - 1500m |
| CO | CL | 500m - 1500m |
| CO | CP | 500m - 1500m |
| CO | CQ | 500m - 1500m |
| CO | CR | 500m - 1500m |
| CO | DN | 500m - 1500m |
| CO | DO | 500m - 1500m |
| CO | DP | 500m - 1500m |
| CO | DQ | 500m - 1500m |
| CO | DR | 500m - 1500m |
| CO | DS | 500m - 1500m |
| CO | DT | 500m - 1500m |
| CO | DU | 500m - 1500m |
| CO | DV | 500m - 1500m |
| CP | BL | 500m - 1500m |
| CP | BM | 500m - 1500m |
| CP | BN | 500m - 1500m |
| CP | BO | 500m - 1500m |
| CP | BP | 500m - 1500m |
| CP | BQ | 500m - 1500m |
| CP | CM | 500m - 1500m |
| CP | CN | 500m - 1500m |
| CP | CO | 500m - 1500m |
| CP | DQ | 500m - 1500m |
| CP | DR | 500m - 1500m |
| CP | DS | 500m - 1500m |
| CP | DT | 500m - 1500m |
| CP | DU | 500m - 1500m |
| CP | DV | 500m - 1500m |
| CQ | BL | 500m - 1500m |
| CQ | BM | 500m - 1500m |
| CQ | BN | 500m - 1500m |
| CQ | BO | 500m - 1500m |
| CQ | BP | 500m - 1500m |
| CQ | BQ | 500m - 1500m |
| CQ | CM | 500m - 1500m |
| CQ | CN | 500m - 1500m |
| CQ | CO | 500m - 1500m |
| CQ | DQ | 500m - 1500m |
| CQ | DR | 500m - 1500m |
| CQ | DS | 500m - 1500m |
| CQ | DT | 500m - 1500m |
| CQ | DU | 500m - 1500m |
| CQ | DV | 500m - 1500m |
| CR | BL | 500m - 1500m |
| CR | BM | 500m - 1500m |
| CR | BN | 500m - 1500m |
| CR | BO | 500m - 1500m |
| CR | BP | 500m - 1500m |
| CR | BQ | 500m - 1500m |
| CR | CM | 500m - 1500m |
| CR | CN | 500m - 1500m |
| CR | CO | 500m - 1500m |
| CR | DQ | 500m - 1500m |
| CR | DR | 500m - 1500m |
| CR | DS | 500m - 1500m |
| CR | DT | 500m - 1500m |
| CR | DU | 500m - 1500m |
| CR | DV | 500m - 1500m |
| CS | BR | 500m - 1500m |
| CS | BS | 500m - 1500m |
| CS | BT | 500m - 1500m |
| CS | BU | 500m - 1500m |
| CS | BV | 500m - 1500m |
| CS | BW | 500m - 1500m |
| CS | BX | 500m - 1500m |
| CS | BY | 500m - 1500m |
| CS | BZ | 500m - 1500m |
| CS | CV | 500m - 1500m |
| CS | CW | 500m - 1500m |
| CS | CX | 500m - 1500m |
| CT | BR | 500m - 1500m |
| CT | BS | 500m - 1500m |
| CT | BT | 500m - 1500m |
| CT | BU | 500m - 1500m |
| CT | BV | 500m - 1500m |
| CT | BW | 500m - 1500m |
| CT | BX | 500m - 1500m |
| CT | BY | 500m - 1500m |
| CT | BZ | 500m - 1500m |
| CT | CV | 500m - 1500m |
| CT | CW | 500m - 1500m |
| CT | CX | 500m - 1500m |
| CU | BR | 500m - 1500m |
| CU | BS | 500m - 1500m |
| CU | BT | 500m - 1500m |
| CU | BU | 500m - 1500m |
| CU | BV | 500m - 1500m |
| CU | BW | 500m - 1500m |
| CU | BX | 500m - 1500m |
| CU | BY | 500m - 1500m |
| CU | BZ | 500m - 1500m |
| CU | CV | 500m - 1500m |
| CU | CW | 500m - 1500m |
| CU | CX | 500m - 1500m |
| CV | BU | 500m - 1500m |
| CV | BV | 500m - 1500m |
| CV | BW | 500m - 1500m |
| CV | BX | 500m - 1500m |
| CV | BY | 500m - 1500m |
| CV | BZ | 500m - 1500m |
| CV | CA | 500m - 1500m |
| CV | CB | 500m - 1500m |
| CV | CC | 500m - 1500m |
| CV | CS | 500m - 1500m |
| CV | CT | 500m - 1500m |
| CV | CU | 500m - 1500m |
| CV | CY | 500m - 1500m |
| CV | CZ | 500m - 1500m |
| CV | DA | 500m - 1500m |
| CW | BU | 500m - 1500m |
| CW | BV | 500m - 1500m |
| CW | BW | 500m - 1500m |
| CW | BX | 500m - 1500m |
| CW | BY | 500m - 1500m |
| CW | BZ | 500m - 1500m |
| CW | CA | 500m - 1500m |
| CW | CB | 500m - 1500m |
| CW | CC | 500m - 1500m |
| CW | CS | 500m - 1500m |
| CW | CT | 500m - 1500m |
| CW | CU | 500m - 1500m |
| CW | CY | 500m - 1500m |
| CW | CZ | 500m - 1500m |
| CW | DA | 500m - 1500m |
| CX | BU | 500m - 1500m |
| CX | BV | 500m - 1500m |
| CX | BW | 500m - 1500m |
| CX | BX | 500m - 1500m |
| CX | BY | 500m - 1500m |
| CX | BZ | 500m - 1500m |
| CX | CA | 500m - 1500m |
| CX | CB | 500m - 1500m |
| CX | CC | 500m - 1500m |
| CX | CS | 500m - 1500m |
| CX | CT | 500m - 1500m |
| CX | CU | 500m - 1500m |
| CX | CY | 500m - 1500m |
| CX | CZ | 500m - 1500m |
| CX | DA | 500m - 1500m |
| CY | BX | 500m - 1500m |
| CY | BY | 500m - 1500m |
| CY | BZ | 500m - 1500m |
| CY | CA | 500m - 1500m |
| CY | CB | 500m - 1500m |
| CY | CC | 500m - 1500m |
| CY | CV | 500m - 1500m |
| CY | CW | 500m - 1500m |
| CY | CX | 500m - 1500m |
| CY | DB | 500m - 1500m |
| CY | DC | 500m - 1500m |
| CY | DD | 500m - 1500m |
| CZ | BX | 500m - 1500m |
| CZ | BY | 500m - 1500m |
| CZ | BZ | 500m - 1500m |
| CZ | CA | 500m - 1500m |
| CZ | CB | 500m - 1500m |
| CZ | CC | 500m - 1500m |
| CZ | CV | 500m - 1500m |
| CZ | CW | 500m - 1500m |
| CZ | CX | 500m - 1500m |
| CZ | DB | 500m - 1500m |
| CZ | DC | 500m - 1500m |
| CZ | DD | 500m - 1500m |
| DA | BX | 500m - 1500m |
| DA | BY | 500m - 1500m |
| DA | BZ | 500m - 1500m |
| DA | CA | 500m - 1500m |
| DA | CB | 500m - 1500m |
| DA | CC | 500m - 1500m |
| DA | CV | 500m - 1500m |
| DA | CW | 500m - 1500m |
| DA | CX | 500m - 1500m |
| DA | DB | 500m - 1500m |
| DA | DC | 500m - 1500m |
| DA | DD | 500m - 1500m |
| DB | CA | 500m - 1500m |
| DB | CB | 500m - 1500m |
| DB | CC | 500m - 1500m |
| DB | CY | 500m - 1500m |
| DB | CZ | 500m - 1500m |
| DB | DA | 500m - 1500m |
| DB | DE | 500m - 1500m |
| DB | DF | 500m - 1500m |
| DB | DG | 500m - 1500m |
| DC | CA | 500m - 1500m |
| DC | CB | 500m - 1500m |
| DC | CC | 500m - 1500m |
| DC | CY | 500m - 1500m |
| DC | CZ | 500m - 1500m |
| DC | DA | 500m - 1500m |
| DC | DE | 500m - 1500m |
| DC | DF | 500m - 1500m |
| DC | DG | 500m - 1500m |
| DD | CA | 500m - 1500m |
| DD | CB | 500m - 1500m |
| DD | CC | 500m - 1500m |
| DD | CY | 500m - 1500m |
| DD | CZ | 500m - 1500m |
| DD | DA | 500m - 1500m |
| DD | DE | 500m - 1500m |
| DD | DF | 500m - 1500m |
| DD | DG | 500m - 1500m |
| DE | DB | 500m - 1500m |
| DE | DC | 500m - 1500m |
| DE | DD | 500m - 1500m |
| DE | DH | 500m - 1500m |
| DE | DI | 500m - 1500m |
| DE | DJ | 500m - 1500m |
| DE | DW | 500m - 1500m |
| DE | DX | 500m - 1500m |
| DE | DY | 500m - 1500m |
| DF | DB | 500m - 1500m |
| DF | DC | 500m - 1500m |
| DF | DD | 500m - 1500m |
| DF | DH | 500m - 1500m |
| DF | DI | 500m - 1500m |
| DF | DJ | 500m - 1500m |
| DF | DW | 500m - 1500m |
| DF | DX | 500m - 1500m |
| DF | DY | 500m - 1500m |
| DG | DB | 500m - 1500m |
| DG | DC | 500m - 1500m |
| DG | DD | 500m - 1500m |
| DG | DH | 500m - 1500m |
| DG | DI | 500m - 1500m |
| DG | DJ | 500m - 1500m |
| DG | DW | 500m - 1500m |
| DG | DX | 500m - 1500m |
| DG | DY | 500m - 1500m |
| DH | CD | 500m - 1500m |
| DH | CE | 500m - 1500m |
| DH | CF | 500m - 1500m |
| DH | DE | 500m - 1500m |
| DH | DF | 500m - 1500m |
| DH | DG | 500m - 1500m |
| DH | DK | 500m - 1500m |
| DH | DL | 500m - 1500m |
| DH | DM | 500m - 1500m |
| DH | DW | 500m - 1500m |
| DH | DX | 500m - 1500m |
| DH | DY | 500m - 1500m |
| DH | DZ | 500m - 1500m |
| DH | EA | 500m - 1500m |
| DH | EB | 500m - 1500m |
| DI | CD | 500m - 1500m |
| DI | CE | 500m - 1500m |
| DI | CF | 500m - 1500m |
| DI | DE | 500m - 1500m |
| DI | DF | 500m - 1500m |
| DI | DG | 500m - 1500m |
| DI | DK | 500m - 1500m |
| DI | DL | 500m - 1500m |
| DI | DM | 500m - 1500m |
| DI | DW | 500m - 1500m |
| DI | DX | 500m - 1500m |
| DI | DY | 500m - 1500m |
| DI | DZ | 500m - 1500m |
| DI | EA | 500m - 1500m |
| DI | EB | 500m - 1500m |
| DJ | CD | 500m - 1500m |
| DJ | CE | 500m - 1500m |
| DJ | CF | 500m - 1500m |
| DJ | DE | 500m - 1500m |
| DJ | DF | 500m - 1500m |
| DJ | DG | 500m - 1500m |
| DJ | DK | 500m - 1500m |
| DJ | DL | 500m - 1500m |
| DJ | DM | 500m - 1500m |
| DJ | DW | 500m - 1500m |
| DJ | DX | 500m - 1500m |
| DJ | DY | 500m - 1500m |
| DJ | DZ | 500m - 1500m |
| DJ | EA | 500m - 1500m |
| DJ | EB | 500m - 1500m |
| DK | CD | 500m - 1500m |
| DK | CE | 500m - 1500m |
| DK | CF | 500m - 1500m |
| DK | CG | 500m - 1500m |
| DK | CH | 500m - 1500m |
| DK | CI | 500m - 1500m |
| DK | DH | 500m - 1500m |
| DK | DI | 500m - 1500m |
| DK | DJ | 500m - 1500m |
| DK | DN | 500m - 1500m |
| DK | DO | 500m - 1500m |
| DK | DP | 500m - 1500m |
| DK | DZ | 500m - 1500m |
| DK | EA | 500m - 1500m |
| DK | EB | 500m - 1500m |
| DK | EC | 500m - 1500m |
| DK | ED | 500m - 1500m |
| DL | CD | 500m - 1500m |
| DL | CE | 500m - 1500m |
| DL | CF | 500m - 1500m |
| DL | CG | 500m - 1500m |
| DL | CH | 500m - 1500m |
| DL | CI | 500m - 1500m |
| DL | DH | 500m - 1500m |
| DL | DI | 500m - 1500m |
| DL | DJ | 500m - 1500m |
| DL | DN | 500m - 1500m |
| DL | DO | 500m - 1500m |
| DL | DP | 500m - 1500m |
| DL | DZ | 500m - 1500m |
| DL | EA | 500m - 1500m |
| DL | EB | 500m - 1500m |
| DL | EC | 500m - 1500m |
| DL | ED | 500m - 1500m |
| DM | CD | 500m - 1500m |
| DM | CE | 500m - 1500m |
| DM | CF | 500m - 1500m |
| DM | CG | 500m - 1500m |
| DM | CH | 500m - 1500m |
| DM | CI | 500m - 1500m |
| DM | DH | 500m - 1500m |
| DM | DI | 500m - 1500m |
| DM | DJ | 500m - 1500m |
| DM | DN | 500m - 1500m |
| DM | DO | 500m - 1500m |
| DM | DP | 500m - 1500m |
| DM | DZ | 500m - 1500m |
| DM | EA | 500m - 1500m |
| DM | EB | 500m - 1500m |
| DM | EC | 500m - 1500m |
| DM | ED | 500m - 1500m |
| DN | CG | 500m - 1500m |
| DN | CH | 500m - 1500m |
| DN | CI | 500m - 1500m |
| DN | CJ | 500m - 1500m |
| DN | CK | 500m - 1500m |
| DN | CL | 500m - 1500m |
| DN | CM | 500m - 1500m |
| DN | CN | 500m - 1500m |
| DN | CO | 500m - 1500m |
| DN | DK | 500m - 1500m |
| DN | DL | 500m - 1500m |
| DN | DM | 500m - 1500m |
| DN | DQ | 500m - 1500m |
| DN | DR | 500m - 1500m |
| DN | DS | 500m - 1500m |
| DN | EC | 500m - 1500m |
| DN | ED | 500m - 1500m |
| DN | EE | 500m - 1500m |
| DN | EF | 500m - 1500m |
| DN | EG | 500m - 1500m |
| DN | EH | 500m - 1500m |
| DN | EI | 500m - 1500m |
| DN | EJ | 500m - 1500m |
| DO | CG | 500m - 1500m |
| DO | CH | 500m - 1500m |
| DO | CI | 500m - 1500m |
| DO | CJ | 500m - 1500m |
| DO | CK | 500m - 1500m |
| DO | CL | 500m - 1500m |
| DO | CM | 500m - 1500m |
| DO | CN | 500m - 1500m |
| DO | CO | 500m - 1500m |
| DO | DK | 500m - 1500m |
| DO | DL | 500m - 1500m |
| DO | DM | 500m - 1500m |
| DO | DQ | 500m - 1500m |
| DO | DR | 500m - 1500m |
| DO | DS | 500m - 1500m |
| DO | EC | 500m - 1500m |
| DO | ED | 500m - 1500m |
| DO | EE | 500m - 1500m |
| DO | EF | 500m - 1500m |
| DO | EG | 500m - 1500m |
| DO | EH | 500m - 1500m |
| DO | EI | 500m - 1500m |
| DO | EJ | 500m - 1500m |
| DP | CG | 500m - 1500m |
| DP | CH | 500m - 1500m |
| DP | CI | 500m - 1500m |
| DP | CJ | 500m - 1500m |
| DP | CK | 500m - 1500m |
| DP | CL | 500m - 1500m |
| DP | CM | 500m - 1500m |
| DP | CN | 500m - 1500m |
| DP | CO | 500m - 1500m |
| DP | DK | 500m - 1500m |
| DP | DL | 500m - 1500m |
| DP | DM | 500m - 1500m |
| DP | DQ | 500m - 1500m |
| DP | DR | 500m - 1500m |
| DP | DS | 500m - 1500m |
| DP | EC | 500m - 1500m |
| DP | ED | 500m - 1500m |
| DP | EE | 500m - 1500m |
| DP | EF | 500m - 1500m |
| DP | EG | 500m - 1500m |
| DP | EH | 500m - 1500m |
| DP | EI | 500m - 1500m |
| DP | EJ | 500m - 1500m |
| DQ | CJ | 500m - 1500m |
| DQ | CK | 500m - 1500m |
| DQ | CL | 500m - 1500m |
| DQ | CM | 500m - 1500m |
| DQ | CN | 500m - 1500m |
| DQ | CO | 500m - 1500m |
| DQ | CP | 500m - 1500m |
| DQ | CQ | 500m - 1500m |
| DQ | CR | 500m - 1500m |
| DQ | DN | 500m - 1500m |
| DQ | DO | 500m - 1500m |
| DQ | DP | 500m - 1500m |
| DQ | DT | 500m - 1500m |
| DQ | DU | 500m - 1500m |
| DQ | DV | 500m - 1500m |
| DQ | EE | 500m - 1500m |
| DQ | EF | 500m - 1500m |
| DQ | EG | 500m - 1500m |
| DQ | EH | 500m - 1500m |
| DQ | EI | 500m - 1500m |
| DQ | EJ | 500m - 1500m |
| DQ | EK | 500m - 1500m |
| DQ | EL | 500m - 1500m |
| DQ | EM | 500m - 1500m |
| DR | CJ | 500m - 1500m |
| DR | CK | 500m - 1500m |
| DR | CL | 500m - 1500m |
| DR | CM | 500m - 1500m |
| DR | CN | 500m - 1500m |
| DR | CO | 500m - 1500m |
| DR | CP | 500m - 1500m |
| DR | CQ | 500m - 1500m |
| DR | CR | 500m - 1500m |
| DR | DN | 500m - 1500m |
| DR | DO | 500m - 1500m |
| DR | DP | 500m - 1500m |
| DR | DT | 500m - 1500m |
| DR | DU | 500m - 1500m |
| DR | DV | 500m - 1500m |
| DR | EE | 500m - 1500m |
| DR | EF | 500m - 1500m |
| DR | EG | 500m - 1500m |
| DR | EH | 500m - 1500m |
| DR | EI | 500m - 1500m |
| DR | EJ | 500m - 1500m |
| DR | EK | 500m - 1500m |
| DR | EL | 500m - 1500m |
| DR | EM | 500m - 1500m |
| DS | CJ | 500m - 1500m |
| DS | CK | 500m - 1500m |
| DS | CL | 500m - 1500m |
| DS | CM | 500m - 1500m |
| DS | CN | 500m - 1500m |
| DS | CO | 500m - 1500m |
| DS | CP | 500m - 1500m |
| DS | CQ | 500m - 1500m |
| DS | CR | 500m - 1500m |
| DS | DN | 500m - 1500m |
| DS | DO | 500m - 1500m |
| DS | DP | 500m - 1500m |
| DS | DT | 500m - 1500m |
| DS | DU | 500m - 1500m |
| DS | DV | 500m - 1500m |
| DS | EE | 500m - 1500m |
| DS | EF | 500m - 1500m |
| DS | EG | 500m - 1500m |
| DS | EH | 500m - 1500m |
| DS | EI | 500m - 1500m |
| DS | EJ | 500m - 1500m |
| DS | EK | 500m - 1500m |
| DS | EL | 500m - 1500m |
| DS | EM | 500m - 1500m |
| DT | CM | 500m - 1500m |
| DT | CN | 500m - 1500m |
| DT | CO | 500m - 1500m |
| DT | CP | 500m - 1500m |
| DT | CQ | 500m - 1500m |
| DT | CR | 500m - 1500m |
| DT | DQ | 500m - 1500m |
| DT | DR | 500m - 1500m |
| DT | DS | 500m - 1500m |
| DT | EH | 500m - 1500m |
| DT | EI | 500m - 1500m |
| DT | EJ | 500m - 1500m |
| DT | EK | 500m - 1500m |
| DT | EL | 500m - 1500m |
| DT | EM | 500m - 1500m |
| DU | CM | 500m - 1500m |
| DU | CN | 500m - 1500m |
| DU | CO | 500m - 1500m |
| DU | CP | 500m - 1500m |
| DU | CQ | 500m - 1500m |
| DU | CR | 500m - 1500m |
| DU | DQ | 500m - 1500m |
| DU | DR | 500m - 1500m |
| DU | DS | 500m - 1500m |
| DU | EH | 500m - 1500m |
| DU | EI | 500m - 1500m |
| DU | EJ | 500m - 1500m |
| DU | EK | 500m - 1500m |
| DU | EL | 500m - 1500m |
| DU | EM | 500m - 1500m |
| DV | CM | 500m - 1500m |
| DV | CN | 500m - 1500m |
| DV | CO | 500m - 1500m |
| DV | CP | 500m - 1500m |
| DV | CQ | 500m - 1500m |
| DV | CR | 500m - 1500m |
| DV | DQ | 500m - 1500m |
| DV | DR | 500m - 1500m |
| DV | DS | 500m - 1500m |
| DV | EH | 500m - 1500m |
| DV | EI | 500m - 1500m |
| DV | EJ | 500m - 1500m |
| DV | EK | 500m - 1500m |
| DV | EL | 500m - 1500m |
| DV | EM | 500m - 1500m |
| DW | DE | 500m - 1500m |
| DW | DF | 500m - 1500m |
| DW | DG | 500m - 1500m |
| DW | DH | 500m - 1500m |
| DW | DI | 500m - 1500m |
| DW | DJ | 500m - 1500m |
| DW | DZ | 500m - 1500m |
| DW | EA | 500m - 1500m |
| DW | EB | 500m - 1500m |
| DX | DE | 500m - 1500m |
| DX | DF | 500m - 1500m |
| DX | DG | 500m - 1500m |
| DX | DH | 500m - 1500m |
| DX | DI | 500m - 1500m |
| DX | DJ | 500m - 1500m |
| DX | DZ | 500m - 1500m |
| DX | EA | 500m - 1500m |
| DX | EB | 500m - 1500m |
| DY | DE | 500m - 1500m |
| DY | DF | 500m - 1500m |
| DY | DG | 500m - 1500m |
| DY | DH | 500m - 1500m |
| DY | DI | 500m - 1500m |
| DY | DJ | 500m - 1500m |
| DY | DZ | 500m - 1500m |
| DY | EA | 500m - 1500m |
| DY | EB | 500m - 1500m |
| DZ | DH | 500m - 1500m |
| DZ | DI | 500m - 1500m |
| DZ | DJ | 500m - 1500m |
| DZ | DK | 500m - 1500m |
| DZ | DL | 500m - 1500m |
| DZ | DM | 500m - 1500m |
| DZ | DW | 500m - 1500m |
| DZ | DX | 500m - 1500m |
| DZ | DY | 500m - 1500m |
| DZ | EC | 500m - 1500m |
| DZ | ED | 500m - 1500m |
| EA | DH | 500m - 1500m |
| EA | DI | 500m - 1500m |
| EA | DJ | 500m - 1500m |
| EA | DK | 500m - 1500m |
| EA | DL | 500m - 1500m |
| EA | DM | 500m - 1500m |
| EA | DW | 500m - 1500m |
| EA | DX | 500m - 1500m |
| EA | DY | 500m - 1500m |
| EA | EC | 500m - 1500m |
| EA | ED | 500m - 1500m |
| EB | DH | 500m - 1500m |
| EB | DI | 500m - 1500m |
| EB | DJ | 500m - 1500m |
| EB | DK | 500m - 1500m |
| EB | DL | 500m - 1500m |
| EB | DM | 500m - 1500m |
| EB | DW | 500m - 1500m |
| EB | DX | 500m - 1500m |
| EB | DY | 500m - 1500m |
| EB | EC | 500m - 1500m |
| EB | ED | 500m - 1500m |
| EC | DK | 500m - 1500m |
| EC | DL | 500m - 1500m |
| EC | DM | 500m - 1500m |
| EC | DN | 500m - 1500m |
| EC | DO | 500m - 1500m |
| EC | DP | 500m - 1500m |
| EC | DZ | 500m - 1500m |
| EC | EA | 500m - 1500m |
| EC | EB | 500m - 1500m |
| EC | EE | 500m - 1500m |
| EC | EF | 500m - 1500m |
| EC | EG | 500m - 1500m |
| ED | DK | 500m - 1500m |
| ED | DL | 500m - 1500m |
| ED | DM | 500m - 1500m |
| ED | DN | 500m - 1500m |
| ED | DO | 500m - 1500m |
| ED | DP | 500m - 1500m |
| ED | DZ | 500m - 1500m |
| ED | EA | 500m - 1500m |
| ED | EB | 500m - 1500m |
| ED | EE | 500m - 1500m |
| ED | EF | 500m - 1500m |
| ED | EG | 500m - 1500m |
| EE | DN | 500m - 1500m |
| EE | DO | 500m - 1500m |
| EE | DP | 500m - 1500m |
| EE | DQ | 500m - 1500m |
| EE | DR | 500m - 1500m |
| EE | DS | 500m - 1500m |
| EE | EC | 500m - 1500m |
| EE | ED | 500m - 1500m |
| EE | EH | 500m - 1500m |
| EE | EI | 500m - 1500m |
| EE | EJ | 500m - 1500m |
| EF | DN | 500m - 1500m |
| EF | DO | 500m - 1500m |
| EF | DP | 500m - 1500m |
| EF | DQ | 500m - 1500m |
| EF | DR | 500m - 1500m |
| EF | DS | 500m - 1500m |
| EF | EC | 500m - 1500m |
| EF | ED | 500m - 1500m |
| EF | EH | 500m - 1500m |
| EF | EI | 500m - 1500m |
| EF | EJ | 500m - 1500m |
| EG | DN | 500m - 1500m |
| EG | DO | 500m - 1500m |
| EG | DP | 500m - 1500m |
| EG | DQ | 500m - 1500m |
| EG | DR | 500m - 1500m |
| EG | DS | 500m - 1500m |
| EG | EC | 500m - 1500m |
| EG | ED | 500m - 1500m |
| EG | EH | 500m - 1500m |
| EG | EI | 500m - 1500m |
| EG | EJ | 500m - 1500m |
| EH | DN | 500m - 1500m |
| EH | DO | 500m - 1500m |
| EH | DP | 500m - 1500m |
| EH | DQ | 500m - 1500m |
| EH | DR | 500m - 1500m |
| EH | DS | 500m - 1500m |
| EH | DT | 500m - 1500m |
| EH | DU | 500m - 1500m |
| EH | DV | 500m - 1500m |
| EH | EE | 500m - 1500m |
| EH | EF | 500m - 1500m |
| EH | EG | 500m - 1500m |
| EH | EK | 500m - 1500m |
| EH | EL | 500m - 1500m |
| EH | EM | 500m - 1500m |
| EI | DN | 500m - 1500m |
| EI | DO | 500m - 1500m |
| EI | DP | 500m - 1500m |
| EI | DQ | 500m - 1500m |
| EI | DR | 500m - 1500m |
| EI | DS | 500m - 1500m |
| EI | DT | 500m - 1500m |
| EI | DU | 500m - 1500m |
| EI | DV | 500m - 1500m |
| EI | EE | 500m - 1500m |
| EI | EF | 500m - 1500m |
| EI | EG | 500m - 1500m |
| EI | EK | 500m - 1500m |
| EI | EL | 500m - 1500m |
| EI | EM | 500m - 1500m |
| EJ | DN | 500m - 1500m |
| EJ | DO | 500m - 1500m |
| EJ | DP | 500m - 1500m |
| EJ | DQ | 500m - 1500m |
| EJ | DR | 500m - 1500m |
| EJ | DS | 500m - 1500m |
| EJ | DT | 500m - 1500m |
| EJ | DU | 500m - 1500m |
| EJ | DV | 500m - 1500m |
| EJ | EE | 500m - 1500m |
| EJ | EF | 500m - 1500m |
| EJ | EG | 500m - 1500m |
| EJ | EK | 500m - 1500m |
| EJ | EL | 500m - 1500m |
| EJ | EM | 500m - 1500m |
| EK | DQ | 500m - 1500m |
| EK | DR | 500m - 1500m |
| EK | DS | 500m - 1500m |
| EK | DT | 500m - 1500m |
| EK | DU | 500m - 1500m |
| EK | DV | 500m - 1500m |
| EK | EH | 500m - 1500m |
| EK | EI | 500m - 1500m |
| EK | EJ | 500m - 1500m |
| EL | DQ | 500m - 1500m |
| EL | DR | 500m - 1500m |
| EL | DS | 500m - 1500m |
| EL | DT | 500m - 1500m |
| EL | DU | 500m - 1500m |
| EL | DV | 500m - 1500m |
| EL | EH | 500m - 1500m |
| EL | EI | 500m - 1500m |
| EL | EJ | 500m - 1500m |
| EM | DQ | 500m - 1500m |
| EM | DR | 500m - 1500m |
| EM | DS | 500m - 1500m |
| EM | DT | 500m - 1500m |
| EM | DU | 500m - 1500m |
| EM | DV | 500m - 1500m |
| EM | EH | 500m - 1500m |
| EM | EI | 500m - 1500m |
| EM | EJ | 500m - 1500m |
You may notice that we have not been provided the coordinates of each location or the actual distance between locations. Rather, the interdistance between locations is given into categorized ranges, named Interdistance class. We discuss this later.
In this section we are going to do some exploratory data analyses (EDA) on the previously-loaded datasets, so we can understand them better and get some insights.
We start with some basic plots showing CPT test registrations, for the training dataset. To improve interpretability, we have also highlighted with the dark step line, one pile location (specifically location EK).
training_EK <- training %>%
filter(`Location ID` == "EK")
plot1 <- training %>%
ggplot(aes(`z [m]`, `qc [MPa]`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
geom_step(data = training_EK, aes(`z [m]`, `qc [MPa]`),
size = 1,
colour = "grey20",
alpha = 1) +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 35), ylim = c(0, 100)) +
theme(panel.grid.minor = element_blank())
plot2 <- training %>%
ggplot(aes(`z [m]`, `fs [MPa]`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
geom_step(data = training_EK, aes(`z [m]`, `fs [MPa]`),
size = 1,
colour = "grey20",
alpha = 1) +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 35), ylim = c(0, 2)) +
labs(x = "") +
theme(panel.grid.minor = element_blank())
plot3 <- training %>%
ggplot(aes(`z [m]`, `u2 [MPa]`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
geom_step(data = training_EK, aes(`z [m]`, `u2 [MPa]`),
size = 1,
colour = "grey20",
alpha = 1) +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 35), ylim = c(-0.5, 0.5)) +
labs(x = "") +
theme(panel.grid.minor = element_blank())
plot1 + plot2 + plot3
CPT test data for training (location EK with dark step line).
Next, we built similar graphs for data related to pile installation.
plot4 <- training %>%
ggplot(aes(`z [m]`, `Blowcount [Blows/m]`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
geom_step(data = training_EK, aes(`z [m]`, `Blowcount [Blows/m]`),
size = 1,
colour = "grey20",
alpha = 1) +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 35), ylim = c(0, 150)) +
theme(panel.grid.minor = element_blank())
plot5 <- training %>%
ggplot(aes(`z [m]`, `Number of blows`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
geom_step(data = training_EK, aes(`z [m]`, `Number of blows`),
size = 1,
colour = "grey20",
alpha = 1) +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 35), ylim = c(0, 2500)) +
labs(x = "") +
theme(panel.grid.minor = element_blank())
plot6 <- training %>%
ggplot(aes(`z [m]`, `Normalised ENTRHU [-]`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
geom_step(data = training_EK, aes(`z [m]`, `Normalised ENTRHU [-]`),
size = 1,
colour = "grey20",
alpha = 1) +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 35), ylim = c(0, 0.70)) +
labs(x = "") +
theme(panel.grid.minor = element_blank())
plot4 + plot5 + plot6
Pile installation data for training(location EK with dark step line).
interdistanceNow we try to explore interdistance data, in order to see if we can get an idea on the general layout or on the geometry of the site. As we previously noted, the interdistance between locations is provided in categories. Let’s see what these categories are.
interdistance <- interdistance %>%
mutate(`Interdistance class` = as_factor(`Interdistance class`))
levels(interdistance$`Interdistance class`)## [1] "<500m" "500m - 1500m" "1500m - 3000m" "3000m - 4500m"
## [5] ">4500m"
We have five categories. Having the pile location data in this form seems a bit unusual, since, for a project, we normally know the exact locations where we expect pile foundations to be installed. This data shortcoming most likely limits our ability to incorporate interdistance into our analysis or even to visualize it. Nonetheless, we can try different visualization layouts.
We transform the data a bit so we can express Interdistance class in numerical terms.
interdistance <- interdistance %>%
mutate(`Interdistance cat` = case_when(`Interdistance class` == "<500m" ~ "A",
`Interdistance class` == "500m - 1500m" ~ "B",
`Interdistance class` == "1500m - 3000m" ~ "C",
`Interdistance class` == "3000m - 4500m" ~ "D",
`Interdistance class` == ">4500m" ~ "E")) %>%
mutate(`Interdistance num` = case_when(`Interdistance cat` == "A" ~ "1.0",
`Interdistance cat` == "B" ~ "0.8",
`Interdistance cat` == "C" ~ "0.6",
`Interdistance cat` == "D" ~ "0.4",
`Interdistance cat` == "E" ~ "0.2")) %>%
mutate(`Interdistance num` = as.double(`Interdistance num`))kable(top_n(interdistance, 100),
digits = 3,
caption = "Interdistance data (edited).",
align = "r") %>%
kable_styling(full_width = T, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6") %>%
scroll_box(height = "300px")| ID location 1 | ID location 2 | Interdistance class | Interdistance cat | Interdistance num |
|---|---|---|---|---|
| AA | AA | <500m | A | 1 |
| AA | AB | <500m | A | 1 |
| AA | AC | <500m | A | 1 |
| AB | AA | <500m | A | 1 |
| AB | AB | <500m | A | 1 |
| AB | AC | <500m | A | 1 |
| AC | AA | <500m | A | 1 |
| AC | AB | <500m | A | 1 |
| AC | AC | <500m | A | 1 |
| AD | AD | <500m | A | 1 |
| AD | AE | <500m | A | 1 |
| AE | AD | <500m | A | 1 |
| AE | AE | <500m | A | 1 |
| AF | AF | <500m | A | 1 |
| AF | AG | <500m | A | 1 |
| AF | AH | <500m | A | 1 |
| AG | AF | <500m | A | 1 |
| AG | AG | <500m | A | 1 |
| AG | AH | <500m | A | 1 |
| AH | AF | <500m | A | 1 |
| AH | AG | <500m | A | 1 |
| AH | AH | <500m | A | 1 |
| AI | AI | <500m | A | 1 |
| AI | AJ | <500m | A | 1 |
| AI | AK | <500m | A | 1 |
| AJ | AI | <500m | A | 1 |
| AJ | AJ | <500m | A | 1 |
| AJ | AK | <500m | A | 1 |
| AK | AI | <500m | A | 1 |
| AK | AJ | <500m | A | 1 |
| AK | AK | <500m | A | 1 |
| AL | AL | <500m | A | 1 |
| AL | AM | <500m | A | 1 |
| AL | AN | <500m | A | 1 |
| AM | AL | <500m | A | 1 |
| AM | AM | <500m | A | 1 |
| AM | AN | <500m | A | 1 |
| AN | AL | <500m | A | 1 |
| AN | AM | <500m | A | 1 |
| AN | AN | <500m | A | 1 |
| AO | AO | <500m | A | 1 |
| AO | AP | <500m | A | 1 |
| AO | AQ | <500m | A | 1 |
| AP | AO | <500m | A | 1 |
| AP | AP | <500m | A | 1 |
| AP | AQ | <500m | A | 1 |
| AQ | AO | <500m | A | 1 |
| AQ | AP | <500m | A | 1 |
| AQ | AQ | <500m | A | 1 |
| AR | AR | <500m | A | 1 |
| AR | AS | <500m | A | 1 |
| AR | AT | <500m | A | 1 |
| AS | AR | <500m | A | 1 |
| AS | AS | <500m | A | 1 |
| AS | AT | <500m | A | 1 |
| AT | AR | <500m | A | 1 |
| AT | AS | <500m | A | 1 |
| AT | AT | <500m | A | 1 |
| AU | AU | <500m | A | 1 |
| AU | AV | <500m | A | 1 |
| AU | AW | <500m | A | 1 |
| AV | AU | <500m | A | 1 |
| AV | AV | <500m | A | 1 |
| AV | AW | <500m | A | 1 |
| AW | AU | <500m | A | 1 |
| AW | AV | <500m | A | 1 |
| AW | AW | <500m | A | 1 |
| AX | AX | <500m | A | 1 |
| AX | AY | <500m | A | 1 |
| AX | AZ | <500m | A | 1 |
| AY | AX | <500m | A | 1 |
| AY | AY | <500m | A | 1 |
| AY | AZ | <500m | A | 1 |
| AZ | AX | <500m | A | 1 |
| AZ | AY | <500m | A | 1 |
| AZ | AZ | <500m | A | 1 |
| BA | BA | <500m | A | 1 |
| BA | BB | <500m | A | 1 |
| BA | BC | <500m | A | 1 |
| BB | BA | <500m | A | 1 |
| BB | BB | <500m | A | 1 |
| BB | BC | <500m | A | 1 |
| BC | BA | <500m | A | 1 |
| BC | BB | <500m | A | 1 |
| BC | BC | <500m | A | 1 |
| BD | BD | <500m | A | 1 |
| BD | BE | <500m | A | 1 |
| BD | BF | <500m | A | 1 |
| BE | BD | <500m | A | 1 |
| BE | BE | <500m | A | 1 |
| BE | BF | <500m | A | 1 |
| BF | BD | <500m | A | 1 |
| BF | BE | <500m | A | 1 |
| BF | BF | <500m | A | 1 |
| BG | BG | <500m | A | 1 |
| BG | BH | <500m | A | 1 |
| BG | BI | <500m | A | 1 |
| BH | BG | <500m | A | 1 |
| BH | BH | <500m | A | 1 |
| BH | BI | <500m | A | 1 |
| BI | BG | <500m | A | 1 |
| BI | BH | <500m | A | 1 |
| BI | BI | <500m | A | 1 |
| BJ | BJ | <500m | A | 1 |
| BJ | BK | <500m | A | 1 |
| BK | BJ | <500m | A | 1 |
| BK | BK | <500m | A | 1 |
| BL | BL | <500m | A | 1 |
| BL | BM | <500m | A | 1 |
| BL | BN | <500m | A | 1 |
| BM | BL | <500m | A | 1 |
| BM | BM | <500m | A | 1 |
| BM | BN | <500m | A | 1 |
| BN | BL | <500m | A | 1 |
| BN | BM | <500m | A | 1 |
| BN | BN | <500m | A | 1 |
| BO | BO | <500m | A | 1 |
| BO | BP | <500m | A | 1 |
| BO | BQ | <500m | A | 1 |
| BP | BO | <500m | A | 1 |
| BP | BP | <500m | A | 1 |
| BP | BQ | <500m | A | 1 |
| BQ | BO | <500m | A | 1 |
| BQ | BP | <500m | A | 1 |
| BQ | BQ | <500m | A | 1 |
| BR | BR | <500m | A | 1 |
| BR | BS | <500m | A | 1 |
| BR | BT | <500m | A | 1 |
| BS | BR | <500m | A | 1 |
| BS | BS | <500m | A | 1 |
| BS | BT | <500m | A | 1 |
| BT | BR | <500m | A | 1 |
| BT | BS | <500m | A | 1 |
| BT | BT | <500m | A | 1 |
| BU | BU | <500m | A | 1 |
| BU | BV | <500m | A | 1 |
| BU | BW | <500m | A | 1 |
| BV | BU | <500m | A | 1 |
| BV | BV | <500m | A | 1 |
| BV | BW | <500m | A | 1 |
| BW | BU | <500m | A | 1 |
| BW | BV | <500m | A | 1 |
| BW | BW | <500m | A | 1 |
| BX | BX | <500m | A | 1 |
| BX | BY | <500m | A | 1 |
| BX | BZ | <500m | A | 1 |
| BY | BX | <500m | A | 1 |
| BY | BY | <500m | A | 1 |
| BY | BZ | <500m | A | 1 |
| BZ | BX | <500m | A | 1 |
| BZ | BY | <500m | A | 1 |
| BZ | BZ | <500m | A | 1 |
| CA | CA | <500m | A | 1 |
| CA | CB | <500m | A | 1 |
| CA | CC | <500m | A | 1 |
| CB | CA | <500m | A | 1 |
| CB | CB | <500m | A | 1 |
| CB | CC | <500m | A | 1 |
| CC | CA | <500m | A | 1 |
| CC | CB | <500m | A | 1 |
| CC | CC | <500m | A | 1 |
| CD | CD | <500m | A | 1 |
| CD | CE | <500m | A | 1 |
| CD | CF | <500m | A | 1 |
| CE | CD | <500m | A | 1 |
| CE | CE | <500m | A | 1 |
| CE | CF | <500m | A | 1 |
| CF | CD | <500m | A | 1 |
| CF | CE | <500m | A | 1 |
| CF | CF | <500m | A | 1 |
| CG | CG | <500m | A | 1 |
| CG | CH | <500m | A | 1 |
| CG | CI | <500m | A | 1 |
| CH | CG | <500m | A | 1 |
| CH | CH | <500m | A | 1 |
| CH | CI | <500m | A | 1 |
| CI | CG | <500m | A | 1 |
| CI | CH | <500m | A | 1 |
| CI | CI | <500m | A | 1 |
| CJ | CJ | <500m | A | 1 |
| CJ | CK | <500m | A | 1 |
| CJ | CL | <500m | A | 1 |
| CK | CJ | <500m | A | 1 |
| CK | CK | <500m | A | 1 |
| CK | CL | <500m | A | 1 |
| CL | CJ | <500m | A | 1 |
| CL | CK | <500m | A | 1 |
| CL | CL | <500m | A | 1 |
| CM | CM | <500m | A | 1 |
| CM | CN | <500m | A | 1 |
| CM | CO | <500m | A | 1 |
| CN | CM | <500m | A | 1 |
| CN | CN | <500m | A | 1 |
| CN | CO | <500m | A | 1 |
| CO | CM | <500m | A | 1 |
| CO | CN | <500m | A | 1 |
| CO | CO | <500m | A | 1 |
| CP | CP | <500m | A | 1 |
| CP | CQ | <500m | A | 1 |
| CP | CR | <500m | A | 1 |
| CQ | CP | <500m | A | 1 |
| CQ | CQ | <500m | A | 1 |
| CQ | CR | <500m | A | 1 |
| CR | CP | <500m | A | 1 |
| CR | CQ | <500m | A | 1 |
| CR | CR | <500m | A | 1 |
| CS | CS | <500m | A | 1 |
| CS | CT | <500m | A | 1 |
| CS | CU | <500m | A | 1 |
| CT | CS | <500m | A | 1 |
| CT | CT | <500m | A | 1 |
| CT | CU | <500m | A | 1 |
| CU | CS | <500m | A | 1 |
| CU | CT | <500m | A | 1 |
| CU | CU | <500m | A | 1 |
| CV | CV | <500m | A | 1 |
| CV | CW | <500m | A | 1 |
| CV | CX | <500m | A | 1 |
| CW | CV | <500m | A | 1 |
| CW | CW | <500m | A | 1 |
| CW | CX | <500m | A | 1 |
| CX | CV | <500m | A | 1 |
| CX | CW | <500m | A | 1 |
| CX | CX | <500m | A | 1 |
| CY | CY | <500m | A | 1 |
| CY | CZ | <500m | A | 1 |
| CY | DA | <500m | A | 1 |
| CZ | CY | <500m | A | 1 |
| CZ | CZ | <500m | A | 1 |
| CZ | DA | <500m | A | 1 |
| DA | CY | <500m | A | 1 |
| DA | CZ | <500m | A | 1 |
| DA | DA | <500m | A | 1 |
| DB | DB | <500m | A | 1 |
| DB | DC | <500m | A | 1 |
| DB | DD | <500m | A | 1 |
| DC | DB | <500m | A | 1 |
| DC | DC | <500m | A | 1 |
| DC | DD | <500m | A | 1 |
| DD | DB | <500m | A | 1 |
| DD | DC | <500m | A | 1 |
| DD | DD | <500m | A | 1 |
| DE | DE | <500m | A | 1 |
| DE | DF | <500m | A | 1 |
| DE | DG | <500m | A | 1 |
| DF | DE | <500m | A | 1 |
| DF | DF | <500m | A | 1 |
| DF | DG | <500m | A | 1 |
| DG | DE | <500m | A | 1 |
| DG | DF | <500m | A | 1 |
| DG | DG | <500m | A | 1 |
| DH | DH | <500m | A | 1 |
| DH | DI | <500m | A | 1 |
| DH | DJ | <500m | A | 1 |
| DI | DH | <500m | A | 1 |
| DI | DI | <500m | A | 1 |
| DI | DJ | <500m | A | 1 |
| DJ | DH | <500m | A | 1 |
| DJ | DI | <500m | A | 1 |
| DJ | DJ | <500m | A | 1 |
| DK | DK | <500m | A | 1 |
| DK | DL | <500m | A | 1 |
| DK | DM | <500m | A | 1 |
| DL | DK | <500m | A | 1 |
| DL | DL | <500m | A | 1 |
| DL | DM | <500m | A | 1 |
| DM | DK | <500m | A | 1 |
| DM | DL | <500m | A | 1 |
| DM | DM | <500m | A | 1 |
| DN | DN | <500m | A | 1 |
| DN | DO | <500m | A | 1 |
| DN | DP | <500m | A | 1 |
| DO | DN | <500m | A | 1 |
| DO | DO | <500m | A | 1 |
| DO | DP | <500m | A | 1 |
| DP | DN | <500m | A | 1 |
| DP | DO | <500m | A | 1 |
| DP | DP | <500m | A | 1 |
| DQ | DQ | <500m | A | 1 |
| DQ | DR | <500m | A | 1 |
| DQ | DS | <500m | A | 1 |
| DR | DQ | <500m | A | 1 |
| DR | DR | <500m | A | 1 |
| DR | DS | <500m | A | 1 |
| DS | DQ | <500m | A | 1 |
| DS | DR | <500m | A | 1 |
| DS | DS | <500m | A | 1 |
| DT | DT | <500m | A | 1 |
| DT | DU | <500m | A | 1 |
| DT | DV | <500m | A | 1 |
| DU | DT | <500m | A | 1 |
| DU | DU | <500m | A | 1 |
| DU | DV | <500m | A | 1 |
| DV | DT | <500m | A | 1 |
| DV | DU | <500m | A | 1 |
| DV | DV | <500m | A | 1 |
| DW | DW | <500m | A | 1 |
| DW | DX | <500m | A | 1 |
| DW | DY | <500m | A | 1 |
| DX | DW | <500m | A | 1 |
| DX | DX | <500m | A | 1 |
| DX | DY | <500m | A | 1 |
| DY | DW | <500m | A | 1 |
| DY | DX | <500m | A | 1 |
| DY | DY | <500m | A | 1 |
| DZ | DZ | <500m | A | 1 |
| DZ | EA | <500m | A | 1 |
| DZ | EB | <500m | A | 1 |
| EA | DZ | <500m | A | 1 |
| EA | EA | <500m | A | 1 |
| EA | EB | <500m | A | 1 |
| EB | DZ | <500m | A | 1 |
| EB | EA | <500m | A | 1 |
| EB | EB | <500m | A | 1 |
| EC | EC | <500m | A | 1 |
| EC | ED | <500m | A | 1 |
| ED | EC | <500m | A | 1 |
| ED | ED | <500m | A | 1 |
| EE | EE | <500m | A | 1 |
| EE | EF | <500m | A | 1 |
| EE | EG | <500m | A | 1 |
| EF | EE | <500m | A | 1 |
| EF | EF | <500m | A | 1 |
| EF | EG | <500m | A | 1 |
| EG | EE | <500m | A | 1 |
| EG | EF | <500m | A | 1 |
| EG | EG | <500m | A | 1 |
| EH | EH | <500m | A | 1 |
| EH | EI | <500m | A | 1 |
| EH | EJ | <500m | A | 1 |
| EI | EH | <500m | A | 1 |
| EI | EI | <500m | A | 1 |
| EI | EJ | <500m | A | 1 |
| EJ | EH | <500m | A | 1 |
| EJ | EI | <500m | A | 1 |
| EJ | EJ | <500m | A | 1 |
| EK | EK | <500m | A | 1 |
| EK | EL | <500m | A | 1 |
| EK | EM | <500m | A | 1 |
| EL | EK | <500m | A | 1 |
| EL | EL | <500m | A | 1 |
| EL | EM | <500m | A | 1 |
| EM | EK | <500m | A | 1 |
| EM | EL | <500m | A | 1 |
| EM | EM | <500m | A | 1 |
We use the ggraph and the igraph packages to further visualize interdistance data.
interdistance %>%
graph_from_data_frame() %>%
ggraph() +
geom_edge_link(alpha = 1/100) +
geom_node_point(size = 2) +
geom_node_text(aes(label = name), vjust = 1.25, hjust = 1.25, size = 2.5) +
theme_void()
Interdistance between all locations considering all levels of Interdistance class.
Nice viz but difficult to interpret. We can get a more insightful plot by considering just interdistances of categories <500m and 500m - 1500m.
interdistance %>%
filter(`Interdistance cat` %in% c("A", "B")) %>%
graph_from_data_frame() %>%
ggraph() +
geom_edge_link(aes(alpha = `Interdistance num`)) +
geom_node_point(size = 2) +
geom_node_text(aes(label = name), vjust = 1.25, hjust = 1.25, size = 2.5) +
theme_void() +
theme(legend.position = "none")
Interdistance between all locations considering Interdistance class of <500m and 500m - 1500m only.
From the above graph, we notice the creation of some “clusters”, each containing 3 piles, representing the piles used for each jacket. Within each of these clusters, the Interdistance class is equal to <500m. In other words, when the Interdistance class between piles is <500m, they are basically the same location, as they belong to the same jacket. Let’s see how the CPT and installation data differ for piles within the same jacket. Consider locations DB, DC, and DD.
training_DBDCDD <- training %>%
filter(`Location ID` %in% c("DB", "DC", "DD"))
plot7 <- training %>%
ggplot(aes(`z [m]`, `qc [MPa]`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
geom_step(data = training_DBDCDD, aes(`z [m]`, `qc [MPa]`, group = `Location ID`, colour = `Location ID`),
size = 1,
alpha = 1) +
scale_colour_manual(values = c("red", "yellow1", "grey20")) +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 35), ylim = c(0, 100)) +
theme(legend.position = "none",
panel.grid.minor = element_blank())
plot8 <- training %>%
ggplot(aes(`z [m]`, `Blowcount [Blows/m]`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
geom_step(data = training_DBDCDD, aes(`z [m]`, `Blowcount [Blows/m]`, group = `Location ID`, colour = `Location ID`),
size = 1,
alpha = 1) +
scale_colour_manual(values = c("red", "yellow1", "grey20")) +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 35), ylim = c(0, 150)) +
theme(legend.position = "none",
panel.grid.minor = element_blank()) +
labs(x = "")
plot9 <- training %>%
ggplot(aes(`z [m]`, `Number of blows`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
geom_step(data = training_DBDCDD, aes(`z [m]`, `Number of blows`, group = `Location ID`, colour = `Location ID`),
size = 1,
alpha = 1) +
scale_colour_manual(values = c("red", "yellow1", "grey20")) +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 35), ylim = c(0, 2500)) +
theme(legend.position = c(0.75, 0.8),
panel.grid.minor = element_blank()) +
labs(x = "")
plot7 + plot8 + plot9
Plot of data for locations DB, DC, and DD.
From the above graphs we can infeer that the same CPT test is used for the three piles of the jacket, since the CPT profiles of all three locations completely overlap. Nonetheless, noticeable differencies are observed in the Blowcount [Blows/m] values. These diferencies represent mostly the spatial variations of the soil conditions between piles of the same jacket.
Another way of looking at this is to emphasize that, although we have data about the installation of 114 piles, the CPT data are relatively scarce. Take the validation dataset, for example. Although it includes 20 locations, there are only seven CPT registrations. This is very likely to limitate the accuracy of our hypothetical predictive model.
The same plot as above but with separated locations for training and validation datasets.
interdistance_tr <- interdistance %>%
filter(`ID location 1` %in% training$`Location ID`) %>%
mutate(Category = "training")
interdistance_va <- interdistance %>%
filter(`ID location 1` %in% validation$`Location ID`) %>%
mutate(Category = "validation")
interdistance_all <- full_join(interdistance_tr, interdistance_va)
interdistance_all %>%
filter(`Interdistance cat` %in% c("A", "B")) %>%
graph_from_data_frame() %>%
ggraph() +
geom_edge_link(aes(alpha = `Interdistance num`)) +
geom_node_point(size = 2) +
geom_node_text(aes(label = name), vjust = 1.25, hjust = 1.25, size = 2.5) +
theme_void() +
facet_wrap(~ Category, nrow = 2) +
theme(legend.position = "none")
Interdistance between all locations considering Interdistance class of <500m and 500m - 1500m only (keeping training and validation separated).
The previous graph gives an idea on how the locations in training and valiation are related to each other, in terms of interdistance. Consider the jacket that contains locations CG, CH and CI, from the validation dataset. We see that, the only other jacket from validation that is closer to 1500m with this jacket is the one that contains locations BJ and BK. All the other jacket from validation, like the one with locations DT, DU and DV, is further than 1500m. Not sure if we can correlate soil properties up to these distances.
Further analyses and considerations on the interdistance data can be made and can be incorporated in the model-building process.
training vs validation dataWe make a simple plot of qc [MPA] vs z [m] for training and validation datasets, just to see if there are any significant differencies.
plot13 <- training %>%
ggplot(aes(`z [m]`, `qc [MPa]`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
geom_point(data = validation, aes(`z [m]`, `qc [MPa]`),
alpha = 1/20, colour = "steelblue") +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 35), ylim = c(0, 100)) +
theme(panel.grid.minor = element_blank(),
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "training + validation")
plot14 <- training %>%
ggplot(aes(`z [m]`, `qc [MPa]`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
geom_point(data = validation, aes(`z [m]`, `qc [MPa]`),
alpha = 1/20, colour = "steelblue") +
geom_point(data = training, aes(`z [m]`, `qc [MPa]`),
alpha = 1,
colour = "grey20") +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 35), ylim = c(0, 100)) +
labs(x = "") +
theme(panel.grid.minor = element_blank(),
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "training")
plot15 <- training %>%
ggplot(aes(`z [m]`, `qc [MPa]`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
geom_point(data = validation, aes(`z [m]`, `qc [MPa]`),
alpha = 1/20, colour = "steelblue") +
geom_point(data = validation, aes(`z [m]`, `qc [MPa]`),
alpha = 1,
colour = "grey20") +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 35), ylim = c(0, 100)) +
labs(x = "") +
theme(panel.grid.minor = element_blank(),
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "validation")
plot13 + plot14 + plot15
qc [MPA] vs z [m] for training and validation.
For the purpose of our work, it is particularly important to understand the relationship between Blowcount [Blows/m] and all the features (i.e. predictors), so we get a better idea which features we will actually include in the model. A good starting point is to build a scatterplot matrix of the training dataset.
ggplot(training, aes(.panel_x, .panel_y)) +
geom_point(alpha = 1/5, size = 0.5, colour = "steelblue") +
facet_matrix(vars(`Blowcount [Blows/m]`, `z [m]`, `qc [MPa]`, `fs [MPa]`, `u2 [MPa]`, `Normalised ENTRHU [-]`, `Bottom wall thickness [mm]`, `Pile penetration [m]`)) +
theme(panel.grid.minor = element_blank(),
strip.background = element_blank(),
strip.text = element_text(colour = "grey20"))
Scatterplot matrix for training.
Notice that we have not included those parameters we know to not have any meaningful influence or that are duplicates. From the previous graph, we can grasp the relationship between Blowcount [Blows/m] and other parameters. For some parameters(z [m], Normalised ENTRHU [-]), some pattern is visually recognisable, while for other parameters, recognizing a clear pattern is not that obvious. All this can be numerically expressed through a correlation plot.
training_corr <- training %>%
dplyr::select(`Blowcount [Blows/m]`, `z [m]`, `qc [MPa]`, `fs [MPa]`, `u2 [MPa]`, `Normalised ENTRHU [-]`, `Bottom wall thickness [mm]`, `Pile penetration [m]`)
corr_matrix <- cor(training_corr)
corrplot(corr_matrix,
method = "color",
type = "lower",
tl.col = "black",
col = brewer.pal(n = 10, name = "RdYlBu"),
bg = "red",
addCoef.col = "grey20",
addgrid.col = "grey20",
cl.cex = .75,
tl.cex = .75,
number.cex = 0.65,
number.digits = 3,
diag = FALSE)
Correlations between features for training.
By looking at the first column of the correlation plot, Blowcount [Blows/m] looks particularly well-correlated to both z [m] and Normalised ENTRHU [-]. Maybe surprisingly, the correlation with the CPT test parameters isn’t so strong (the coefficient of correlation barely passes the 0.5 mark, for qc [MPa]).
Now we try an approach to introduce engineering/geotechnical knowledge by means of creating some additional variable (i.e. feature) which simultaneously provide engineering insight and potentially have better predictive performance. We use the available data to estimate two parameters:
friction angle [°] - using the Kulhawy and Mayne 1990 relationship and,skin friction API [kPa] - which is the value of the unit skin friction according to the relationship propossed by API.We do this for both, training and validation.The Vertical effective stress [kPa] is also neccesary for these assesments and it is provided within the normalised data. Some preparatory and data manipulation is needed at this point, especially considering that the normalised data contain few missing values (NA). Various approaches can be used to deal with the NA values (removing them, replacing with the average value, etc) and here we choose to replace them with the next row value. Notice that the friction angle [°] is used here as an input in the estimation of skin friction API [kPa], not as a predicting feature per se.
vert_estress_tr <- training_no %>%
mutate(`Ic [-]` = replace(`Ic [-]`, which(is.na(`Qt [-]`)), NA)) %>%
fill(`area ratio [-]`, `qt [MPa]`, `Delta u2 [MPa]`, `Rf [%]`, `Bq [-]`, `Qt [-]`, `Fr [%]`, `qnet [MPa]`, `Ic [-]`, .direction = "up") %>%
dplyr::select(`ID`, `Vertical effective stress [kPa]`)
training <- training %>%
inner_join(vert_estress_tr, by = "ID") %>%
mutate(`friction angle [°]` = 17.6 + 11 * log((`qc [MPa]`/0.1)/(sqrt(`Vertical effective stress [kPa]`/0.1))),
`skin friction API [kPa]` = 0.8 * `Vertical effective stress [kPa]` * tan((`friction angle [°]` - 5)*3.14/180))vert_estress_va <- validation_no %>%
mutate(`Ic [-]` = replace(`Ic [-]`, which(is.na(`Qt [-]`)), NA)) %>%
fill(`area ratio [-]`, `qt [MPa]`, `Delta u2 [MPa]`, `Rf [%]`, `Bq [-]`, `Qt [-]`, `Fr [%]`, `qnet [MPa]`, `Ic [-]`, .direction = "up") %>%
dplyr::select(`ID`, `Vertical effective stress [kPa]`)
validation <- validation %>%
inner_join(vert_estress_va, by = "ID") %>%
mutate(`friction angle [°]` = 17.6 + 11 * log((`qc [MPa]`/0.1)/(sqrt(`Vertical effective stress [kPa]`/0.1))),
`skin friction API [kPa]` = 0.8 * `Vertical effective stress [kPa]` * tan((`friction angle [°]` - 5)*3.14/180))We can visualize the difference between skin friction API [kPa] and qc [MPa] in terms of their relationship with Blowcount [Blows/m]. The next graphs suggest that skin friction API [kPa] is a better predictor for `Blowcount [Blows/m], having a value of the coefficient of correlation around 0.72.
plot16 <- training %>%
ggplot(aes(`qc [MPa]`, `Blowcount [Blows/m]`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 100), ylim = c(0, 150)) +
theme(panel.grid.minor = element_blank())
plot17 <- training %>%
ggplot(aes(`fs [MPa]`, `Blowcount [Blows/m]`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 2), ylim = c(0, 150)) +
theme(panel.grid.minor = element_blank())
plot18 <- training %>%
ggplot(aes(`skin friction API [kPa]`, `Blowcount [Blows/m]`)) +
geom_point(alpha = 1/20, colour = "steelblue") +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 250), ylim = c(0, 150)) +
theme(panel.grid.minor = element_blank())
plot16 + plot17 + plot18
Comparison of Blowcount [Blows/m]’s relationship with skin friction API [kPa] to qc [MPa] and fs [MPa].
training %>%
dplyr::select(`Blowcount [Blows/m]`, `skin friction API [kPa]`) %>%
cor() %>%
kable(digits = 3,
align = "r") %>%
kable_styling(full_width = F, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6")| Blowcount [Blows/m] | skin friction API [kPa] | |
|---|---|---|
| Blowcount [Blows/m] | 1.000 | 0.717 |
| skin friction API [kPa] | 0.717 | 1.000 |
We now continue with the model-building process, starting from some simple linear models and then progressing to more advanced ones.
training dataThe first thing we do is to randomly splitt the training data into:
train - used to train the model/machine,test - used to evaluate how the model performs on new data.Notice that we have also another set of data, validation which is used to evaluate the model performance in the context of the competition we introduced at the top of this post.
training <- training %>%
mutate(`Location ID` = as_factor(`Location ID`))
training_locations <- levels(training$`Location ID`)
set.seed(111)
test_locations <- sample(training_locations, 18)
test <- training %>% filter(`Location ID` %in% test_locations)
train <- training %>% filter (!(`Location ID` %in% test_locations))Our first attempt involve considering linear regression models.
The idea of a Simple Linear Regression is to predict Blowcount [Blows/m] considering only one feature. We use Normalised ENTRHU [-].
model_slr <- lm(`Blowcount [Blows/m]` ~ `Normalised ENTRHU [-]`, data = train)
model_slr %>%
glance() %>%
kable(digits = 8,
align = "r") %>%
kable_styling(full_width = T, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6")| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.4475658 | 0.4474183 | 22.19974 | 3034.898 | 0 | 2 | -16936.28 | 33878.57 | 33897.25 | 1846136 | 3746 |
model_slr %>%
tidy() %>%
kable(digits = 8,
align = "r") %>%
kable_styling(full_width = T, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6")| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 20.9068 | 0.8842616 | 23.64323 | 0 |
Normalised ENTRHU [-]
|
119.2639 | 2.1648957 | 55.08991 | 0 |
The above tables present some of the key features of the model, like adj.r.squared, p.value, Intercept’s estimate, Intercept’s std.error, etc. These values are useful to evaluate how the model performs. The reference metric, as described in the competition evaluation section, is the RMSE (Root Mean Square Error). Lower the RMSE value, the better. We can estimate RMSE as follows.
train_aug_slr <- model_slr %>%
augment(data = train)
sqrt(sum((train_aug_slr$`Blowcount..Blows.m.` - train_aug_slr$.fitted)^2)/nrow(train_aug_slr))## [1] 22.19382
plot_slr1 <- train_aug_slr %>%
ggplot(aes(`Normalised.ENTRHU....`, `Blowcount..Blows.m.`)) +
geom_point(alpha = 1/20) +
geom_point(aes(y = .fitted), color = "steelblue", alpha = 1) +
expand_limits(x = 0, y = 0) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Model fit vs actual data.",
x = "Normalised ENTRHU [-]",
y = "Blowcount [Blows/m]")
plot_slr2 <- train_aug_slr %>%
ggplot(aes(.fitted, `Blowcount..Blows.m.`)) +
geom_point(alpha = 1/20) +
expand_limits(x = 0, y = 0) +
geom_abline(color = "grey20", size = 1) +
coord_fixed(xlim = c(0, 150), ylim = c(0, 150)) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Prediction vs actual Blowcount [Blows/m]",
x = "Predicted Blowcount [Blows/m]",
y = "Actual Blowcount [Blows/m]")
plot_slr3 <- train_aug_slr %>%
ggplot(aes(`Normalised.ENTRHU....`, .resid)) +
geom_point(alpha = 1/20) +
geom_hline(aes(yintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Scatter plot of residuals.",
x = "Normalised ENTRHU [-]",
y = "Residuals")
plot_slr4 <- train_aug_slr %>%
ggplot(aes(.resid)) +
geom_histogram(binwidth = 2, alpha = 1/5) +
geom_vline(aes(xintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Histogram of residuals.",
x = "Residuals",
y = "Count")
patch_slr1 <- plot_slr1 + plot_slr2
patch_slr2 <- plot_slr3 + plot_slr4
patch_slr1 / patch_slr2Results for Simple Linear Regression
The above plots give another overview of our model’s results and performance, in visual terms. As it was obvious and as it is suggested by the adj.r.squared value aswell, a Simple Linear Regression model is not likely to be a good fit for our data. In particular, the residuals scatter plot and histograms suggest that the residuals show some pattern (i.e. they are not random). In this context, we would like the residuals histogram to resemble a normal distribution.
A Multiple Linear Regression is similar to the Simple Linear Regression, with the difference that, to predict Blowcount [Blows/m], we now consider more than one feature. We use:
Normalised ENTRHU [-],z [m], andskin friction API [kPa].We choose to use these features based on their relatively good correlation with Blowcount [Blows/m]. Note that more rigorouz and robust procedures for feature selection (i.e. deciding which features to include in the model) exist. One option could be to introduce a regularization technique by means of lasso regression.
The application steps of Multiple Linear Regression are in analogy of those used for Simple Linear Regression.
model_mlr <- lm(`Blowcount [Blows/m]` ~ `Normalised ENTRHU [-]` + `z [m]` + `skin friction API [kPa]`, data = train)
model_mlr %>%
glance() %>%
kable(digits = 8,
align = "r") %>%
kable_styling(full_width = T, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6")| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.5365526 | 0.5361812 | 20.33873 | 1444.862 | 0 | 4 | -16607.13 | 33224.26 | 33255.41 | 1548758 | 3744 |
model_mlr %>%
tidy() %>%
kable(digits = 8,
align = "r") %>%
kable_styling(full_width = T, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6")| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 17.8893563 | 0.82998925 | 21.553720 | 0.000e+00 |
Normalised ENTRHU [-]
|
18.7555493 | 4.29131009 | 4.370588 | 1.273e-05 |
z [m]
|
1.2175538 | 0.11676109 | 10.427736 | 0.000e+00 |
skin friction API [kPa]
|
0.2475385 | 0.01849432 | 13.384571 | 0.000e+00 |
train_aug_mlr <- model_mlr %>%
augment(data = train)
sqrt(sum((train_aug_mlr$`Blowcount..Blows.m.` - train_aug_mlr$.fitted)^2)/nrow(train_aug_mlr))## [1] 20.32788
plot_mlr1 <- train_aug_mlr %>%
ggplot(aes(`Normalised.ENTRHU....`, `Blowcount..Blows.m.`)) +
geom_point(alpha = 1/20) +
geom_point(aes(y = .fitted), color = "steelblue", alpha = 1) +
expand_limits(x = 0, y = 0) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Model fit vs actual data.",
x = "Normalised ENTRHU [-]",
y = "Blowcount [Blows/m]")
plot_mlr2 <- train_aug_mlr %>%
ggplot(aes(.fitted, `Blowcount..Blows.m.`)) +
geom_point(alpha = 1/20) +
expand_limits(x = 0, y = 0) +
geom_abline(color = "grey20", size = 1) +
coord_fixed(xlim = c(0, 150), ylim = c(0, 150)) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Prediction vs actual Blowcount [Blows/m]",
x = "Predicted Blowcount [Blows/m]",
y = "Actual Blowcount [Blows/m]")
plot_mlr3 <- train_aug_mlr %>%
ggplot(aes(`Normalised.ENTRHU....`, .resid)) +
geom_point(alpha = 1/20) +
geom_hline(aes(yintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Scatter plot of residuals.",
x = "Normalised ENTRHU [-]",
y = "Residuals")
plot_mlr4 <- train_aug_mlr %>%
ggplot(aes(.resid)) +
geom_histogram(binwidth = 2, alpha = 1/5) +
geom_vline(aes(xintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Histogram of residuals.",
x = "Residuals",
y = "Count")
patch_mlr1 <- plot_mlr1 + plot_mlr2
patch_mlr2 <- plot_mlr3 + plot_mlr4
patch_mlr1 / patch_mlr2Results for Multiple Linear Regression
We see that, the Multiple Linear Regression model performs a bit better than the Simple Linear Regression model. Yet, we are far from capturing the trend of the data.
Since the relationship between blowcount and depth is obviously not perfectly linear, it’s in our interest to explore other models which account for nonlinearity. We explore Natural Splines, which model relationship of various degrees of freedom. These models are similar to Polynomial Regression models.
We start with a Natural Spline with 2 degrees of freedom and one feature.
model_ns2 <- lm(`Blowcount [Blows/m]` ~ ns(`Normalised ENTRHU [-]`, 2), data = train)
model_ns2 %>%
glance() %>%
kable(digits = 8,
align = "r") %>%
kable_styling(full_width = T, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6")| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.5254787 | 0.5252252 | 20.57754 | 2073.582 | 0 | 3 | -16651.38 | 33310.77 | 33335.68 | 1585765 | 3745 |
model_ns2 %>%
tidy() %>%
kable(digits = 8,
align = "r") %>%
kable_styling(full_width = T, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6")| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 17.73742 | 0.8664459 | 20.471466 | 0 |
ns(Normalised ENTRHU [-], 2)1
|
116.28418 | 1.8555944 | 62.666809 | 0 |
ns(Normalised ENTRHU [-], 2)2
|
15.67978 | 2.0841856 | 7.523216 | 0 |
train_aug_ns2 <- model_ns2 %>%
augment(data = train)
sqrt(sum((train_aug_ns2$`Blowcount..Blows.m.` - train_aug_ns2$.fitted)^2)/nrow(train_aug_ns2))## [1] 20.56931
plot_ns21 <- train_aug_ns2 %>%
ggplot(aes(`Normalised.ENTRHU....`, `Blowcount..Blows.m.`)) +
geom_point(alpha = 1/20) +
geom_point(aes(y = .fitted), color = "steelblue", alpha = 1) +
expand_limits(x = 0, y = 0) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Model fit vs actual data.",
x = "Normalised ENTRHU [-]",
y = "Blowcount [Blows/m]")
plot_ns22 <- train_aug_ns2 %>%
ggplot(aes(.fitted, `Blowcount..Blows.m.`)) +
geom_point(alpha = 1/20) +
expand_limits(x = 0, y = 0) +
geom_abline(color = "grey20", size = 1) +
coord_fixed(xlim = c(0, 150), ylim = c(0, 150)) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Prediction vs actual Blowcount [Blows/m]",
x = "Predicted Blowcount [Blows/m]",
y = "Actual Blowcount [Blows/m]")
plot_ns23 <- train_aug_ns2 %>%
ggplot(aes(`Normalised.ENTRHU....`, .resid)) +
geom_point(alpha = 1/20) +
geom_hline(aes(yintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Scatter plot of residuals.",
x = "Normalised ENTRHU [-]",
y = "Residuals")
plot_ns24 <- train_aug_ns2 %>%
ggplot(aes(.resid)) +
geom_histogram(binwidth = 2, alpha = 1/5) +
geom_vline(aes(xintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Histogram of residuals.",
x = "Residuals",
y = "Count")
patch_ns21 <- plot_ns21 + plot_ns22
patch_ns22 <- plot_ns23 + plot_ns24
patch_ns21 / patch_ns22Results for Natural Spline with 2 degrees of freedom - one feature
We explore the same model using three features.
model_mns2 <- lm(`Blowcount [Blows/m]` ~ ns(`Normalised ENTRHU [-]`, 2) + ns(`z [m]`, 2) + ns(`skin friction API [kPa]`, 2), data = train)
model_mns2 %>%
glance() %>%
kable(digits = 8,
align = "r") %>%
kable_styling(full_width = T, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6")| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.6538748 | 0.6533197 | 17.58386 | 1177.871 | 0 | 7 | -16060.13 | 32136.25 | 32186.08 | 1156688 | 3741 |
model_mns2 %>%
tidy() %>%
kable(digits = 8,
align = "r") %>%
kable_styling(full_width = T, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6")| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -3.479181 | 1.062089 | -3.275789 | 0.00106328 |
ns(Normalised ENTRHU [-], 2)1
|
-4.445504 | 3.972912 | -1.118954 | 0.26323175 |
ns(Normalised ENTRHU [-], 2)2
|
-35.723600 | 2.443284 | -14.621142 | 0.00000000 |
ns(z [m], 2)1
|
60.878212 | 5.404193 | 11.264996 | 0.00000000 |
ns(z [m], 2)2
|
16.451251 | 2.789028 | 5.898560 | 0.00000000 |
ns(skin friction API [kPa], 2)1
|
110.762209 | 4.570665 | 24.233283 | 0.00000000 |
ns(skin friction API [kPa], 2)2
|
41.499418 | 3.439455 | 12.065695 | 0.00000000 |
train_aug_mns2 <- model_mns2 %>%
augment(data = train)
sqrt(sum((train_aug_mns2$`Blowcount..Blows.m.` - train_aug_mns2$.fitted)^2)/nrow(train_aug_mns2))## [1] 17.56743
plot_mns21 <- train_aug_mns2 %>%
ggplot(aes(`Normalised.ENTRHU....`, `Blowcount..Blows.m.`)) +
geom_point(alpha = 1/20) +
geom_point(aes(y = .fitted), color = "steelblue", alpha = 1) +
expand_limits(x = 0, y = 0) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Model fit vs actual data.",
x = "Normalised ENTRHU [-]",
y = "Blowcount [Blows/m]")
plot_mns22 <- train_aug_mns2 %>%
ggplot(aes(.fitted, `Blowcount..Blows.m.`)) +
geom_point(alpha = 1/20) +
expand_limits(x = 0, y = 0) +
geom_abline(color = "grey20", size = 1) +
coord_fixed(xlim = c(0, 150), ylim = c(0, 150)) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Prediction vs actual Blowcount [Blows/m]",
x = "Predicted Blowcount [Blows/m]",
y = "Actual Blowcount [Blows/m]")
plot_mns23 <- train_aug_mns2 %>%
ggplot(aes(`Normalised.ENTRHU....`, .resid)) +
geom_point(alpha = 1/20) +
geom_hline(aes(yintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Scatter plot of residuals.",
x = "Normalised ENTRHU [-]",
y = "Residuals")
plot_mns24 <- train_aug_mns2 %>%
ggplot(aes(.resid)) +
geom_histogram(binwidth = 2, alpha = 1/5) +
geom_vline(aes(xintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Histogram of residuals.",
x = "Residuals",
y = "Count")
patch_mns21 <- plot_mns21 + plot_mns22
patch_mns22 <- plot_mns23 + plot_mns24
patch_mns21 / patch_mns22Results for Natural Spline with 2 degrees of freedom - multiple features
This model already is a significant improvement from the previous ones, achieving an adj.r.squared of 0.67 and a RMSE around 17.1. The residiuals histogram seems too look more like following a normal distribution and it’s also narrower, indicating smaller residual values.
We expect that Natural Spline models with higher degrees of freedom to perform better, considering their higher flexibility. To see how increasing the degrees of freedom impacts the model performance, we explore models with degrees of freedom ranging between 2 and 8, and compare their performances.
models_ns <- tibble(spline_df = 2:8) %>%
mutate(lm_model = map(spline_df, ~ lm(`Blowcount [Blows/m]` ~ ns(`Normalised ENTRHU [-]`, df = .), data = train)))
augmented_unnested <- models_ns %>%
mutate(augmented = map(lm_model, augment, data = train)) %>%
unnest(augmented)
p1 <- augmented_unnested %>%
ggplot(aes(`Normalised.ENTRHU....`, `Blowcount..Blows.m.`)) +
geom_point(alpha = 1/100) +
geom_line(aes(y = .fitted, colour = factor(spline_df)), alpha = 1, size = 1.5) +
expand_limits(x = 0, y = 0) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Model fit vs actual data.",
x = "Normalised ENTRHU [-]",
y = "Blowcount [Blows/m]",
colour = "Degrees of freedom") +
theme(legend.position = "bottom") +
guides(color = guide_legend(title.position = "top"))
glanced_models <- models_ns %>%
mutate(glanced = map(lm_model, glance, data = train)) %>%
unnest(glanced)
p2 <- glanced_models %>%
ggplot() +
geom_segment(aes(x = spline_df, xend = spline_df, y = 0.5, yend = adj.r.squared)) +
geom_point(data = glanced_models, aes(spline_df, adj.r.squared, colour = adj.r.squared), size = 4) +
scale_color_distiller(palette = "Spectral", limits = c(0.52, 0.54)) +
expand_limits(x = 2, y = .5) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
scale_x_continuous(breaks = c(2:10)) +
labs(title = "Models comparison by adjusted R-squared value.",
x = "Number of degrees of freedom",
y = "Adjusted R-squared",
colour = "Adjusted R-squared") +
theme(legend.position = "bottom",
legend.key.width = unit(0.7, "cm")) +
guides(color = guide_legend(title.position = "top"))
p1 + p2Performance comparison for Natural Splines with degrees of freedom from 2 to 8
It looks like there is no significant inprovement in model’s performance afer we apply 4 degrees of freedom. Thus, we continue with a Natural Spline model with 4 degrees of freedom and by using multiple features.
model_mns4 <- lm(`Blowcount [Blows/m]` ~ ns(`Normalised ENTRHU [-]`, 4) + ns(`z [m]`, 4) + ns(`skin friction API [kPa]`, 4), data = train)
model_mns4 %>%
glance() %>%
kable(digits = 8,
align = "r") %>%
kable_styling(full_width = T, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6")| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.6772196 | 0.6761826 | 16.99416 | 653.0279 | 0 | 13 | -15929.27 | 31886.53 | 31973.74 | 1078674 | 3735 |
model_mns4 %>%
tidy() %>%
kable(digits = 8,
align = "r") %>%
kable_styling(full_width = T, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6")| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -8.548995 | 1.833634 | -4.662323 | 0.00000324 |
ns(Normalised ENTRHU [-], 4)1
|
11.885604 | 2.500446 | 4.753394 | 0.00000208 |
ns(Normalised ENTRHU [-], 4)2
|
-21.529732 | 2.412246 | -8.925182 | 0.00000000 |
ns(Normalised ENTRHU [-], 4)3
|
-10.065279 | 4.795385 | -2.098951 | 0.03588818 |
ns(Normalised ENTRHU [-], 4)4
|
8.817843 | 4.138711 | 2.130577 | 0.03318915 |
ns(z [m], 4)1
|
37.744106 | 3.378004 | 11.173493 | 0.00000000 |
ns(z [m], 4)2
|
34.078242 | 3.019748 | 11.285128 | 0.00000000 |
ns(z [m], 4)3
|
83.329878 | 7.092886 | 11.748374 | 0.00000000 |
ns(z [m], 4)4
|
30.869633 | 3.730955 | 8.273922 | 0.00000000 |
ns(skin friction API [kPa], 4)1
|
55.549457 | 2.725780 | 20.379291 | 0.00000000 |
ns(skin friction API [kPa], 4)2
|
45.586043 | 2.814827 | 16.194971 | 0.00000000 |
ns(skin friction API [kPa], 4)3
|
84.954922 | 6.100541 | 13.925801 | 0.00000000 |
ns(skin friction API [kPa], 4)4
|
68.072852 | 4.307986 | 15.801548 | 0.00000000 |
train_aug_mns4 <- model_mns4 %>%
augment(data = train)
sqrt(sum((train_aug_mns4$`Blowcount..Blows.m.` - train_aug_mns4$.fitted)^2)/nrow(train_aug_mns4))## [1] 16.96467
plot_mns41 <- train_aug_mns4 %>%
ggplot(aes(`Normalised.ENTRHU....`, `Blowcount..Blows.m.`)) +
geom_point(alpha = 1/20) +
geom_point(aes(y = .fitted), color = "steelblue", alpha = 1) +
expand_limits(x = 0, y = 0) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Model fit vs actual data.",
x = "Normalised ENTRHU [-]",
y = "Blowcount [Blows/m]")
plot_mns42 <- train_aug_mns4 %>%
ggplot(aes(.fitted, `Blowcount..Blows.m.`)) +
geom_point(alpha = 1/20) +
expand_limits(x = 0, y = 0) +
geom_abline(color = "grey20", size = 1) +
coord_fixed(xlim = c(0, 150), ylim = c(0, 150)) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Prediction vs actual Blowcount [Blows/m]",
x = "Predicted Blowcount [Blows/m]",
y = "Actual Blowcount [Blows/m]")
plot_mns43 <- train_aug_mns4 %>%
ggplot(aes(`Normalised.ENTRHU....`, .resid)) +
geom_point(alpha = 1/20) +
geom_hline(aes(yintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Scatter plot of residuals.",
x = "Normalised ENTRHU [-]",
y = "Residuals")
plot_mns44 <- train_aug_mns4 %>%
ggplot(aes(.resid)) +
geom_histogram(binwidth = 2, alpha = 1/5) +
geom_vline(aes(xintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Histogram of residuals.",
x = "Residuals",
y = "Count")
patch_mns41 <- plot_mns41 + plot_mns42
patch_mns42 <- plot_mns43 + plot_mns44
patch_mns41 / patch_mns42Results for Natural Spline with 4 degrees of freedom - multiple features
test datasetThe last model we proposed, having an adj.r.squared of 0.67 and a RMSE around 16.9, seems to be the best-performing model so far. We validate it’s performance against the test dataset. Note that this is the reason we indeed created this dataset, separating it from the initial training data.
test$predictions <- predict(model_mns4, newdata = test)
sqrt(sum((test$'Blowcount [Blows/m]' - test$predictions)^2)/nrow(test))## [1] 15.26106
plot_t1 <- test %>%
ggplot(aes(`Normalised ENTRHU [-]`, `Blowcount [Blows/m]`)) +
geom_point(alpha = 1/20) +
geom_point(data = test, aes(`Normalised ENTRHU [-]`, predictions), color = "steelblue", alpha = 1) +
expand_limits(x = 0, y = 0) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Model fit vs actual data.",
x = "Normalised ENTRHU [-]",
y = "Blowcount [Blows/m]")
plot_t2 <- test %>%
ggplot(aes(predictions, `Blowcount [Blows/m]`)) +
geom_point(alpha = 1/20) +
expand_limits(x = 0, y = 0) +
geom_abline(color = "grey20", size = 1) +
coord_fixed(xlim = c(0, 150), ylim = c(0, 150)) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Prediction vs actual Blowcount [Blows/m]",
x = "Predicted Blowcount [Blows/m]",
y = "Actual Blowcount [Blows/m]")
plot_t3 <- test %>%
ggplot(aes(`Normalised ENTRHU [-]`, predictions - `Blowcount [Blows/m]`)) +
geom_point(alpha = 1/20) +
geom_hline(aes(yintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Scatter plot of residuals.",
x = "Normalised ENTRHU [-]",
y = "Residuals")
plot_t4 <- test %>%
ggplot(aes(predictions - `Blowcount [Blows/m]`)) +
geom_histogram(binwidth = 2, alpha = 1/5) +
geom_vline(aes(xintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Histogram of residuals.",
x = "Residuals",
y = "Count")
patch_t1 <- plot_t1 + plot_t2
patch_t2 <- plot_t3 + plot_t4
patch_t1 / patch_t2
Results for Natural Spline with 4 degrees of freedom - multiple features - applied to the test dataset
The RMSE value of 15.2 and the above plots suggest that the model performs well and consistently against the test dataset.
Let’s plot predicted vs actual Blowcount [Blows/m] for few CPT locations from test.
test %>%
ggplot(aes(`z [m]`, `Blowcount [Blows/m]`)) +
geom_point(alpha = 1/10) +
geom_step(data = test, aes(`z [m]`, predictions, group = `Location ID`),
size = 1,
alpha = 1,
colour = "steelblue") +
scale_x_reverse() +
scale_y_continuous(position = "right") +
coord_flip(xlim = c(0, 30), ylim = c(0, 150)) +
theme(panel.grid.minor = element_blank(),
strip.background = element_blank(),
strip.text = element_text(colour = "grey20"),
strip.placement = "outside") +
facet_wrap(~`Location ID`, ncol = 6)
Predicted (blue stepped line) vs actual (grey dots) Blowcount [Blows/m] for test locations.
Generally speaking, it looks like the predicted Blowcount [Blows/m] follow the actual values very well. Nonetheless, this is not the case for every Location ID. In few cases, the model considerably overestimates or underestimates the actual data. Further model improvements could address this issue.
The following plot shows the how the total Number of blows for each Location ID differs between the predicted and actual values.
test %>%
group_by(`Location ID`) %>%
summarise(actual_blows = sum(`Blowcount [Blows/m]`),
predicted_blows = sum(predictions)) %>%
ggplot(aes(predicted_blows, actual_blows)) +
geom_point(alpha = 1, size = 5, aes(colour = abs(predicted_blows - actual_blows)/actual_blows)) +
scale_colour_distiller(palette = "Spectral", limits = c(0, 0.4), labels = percent) +
coord_fixed(xlim = c(1800, 4000), ylim = c(1800, 4000)) +
theme(panel.grid.minor = element_blank(),
legend.key.height = unit(2.25, "cm")) +
labs(x = "Predicted number of blows",
y = "Actual number of blows",
colour = "Error") +
geom_abline(color = "grey20", size = 1)
Predicted vs actual number of total blows for test locations.
We see that the relative error in predicting the total number of blows for each location normally lies between 0% and 20%, with a median error near 10.5%.
test %>%
group_by(`Location ID`) %>%
summarise(actual_blows = sum(`Blowcount [Blows/m]`),
predicted_blows = sum(predictions)) %>%
summarise(median_error = median(abs(predicted_blows - actual_blows)/actual_blows))## # A tibble: 1 x 1
## median_error
## <dbl>
## 1 0.106
In this section we investigate models that are inherently nonlinear in nature. The advantage of using these models rely on the fact that we don’t need to know or specify the nonlinearity of the data prior to model training. Among others, we consider models like neural networks, support vector machines, and random forests. The analysis is done within the framework of the caret package, which is a comprehensive and incredibly powerful machine learning tool. It provides a uniform interface to build predictive models and, among other, it cointain tools/functions for data splitting, pre-processing, feature selection model tuning using resampling, variable importance estimation, etc. To date, 238 models are available within the `caret’ package.
Note that we are not going into the details of the caret package. Also, not much elaboration is provided on the used models. For more on this please se the caret package documentation here and here.
Before we continue with model training, we need to prepare the scene a bit.
control_object.We train the models here and then we discuss on their performance.
set.seed(111)
gbm <- train(`Blowcount [Blows/m]` ~ `Normalised ENTRHU [-]` + `z [m]` + `skin friction API [kPa]`,
data = train,
method = "gbm",
tuneGrid = expand.grid(interaction.depth = seq(1, 7, by = 2),
n.trees = seq(100, 1000, by = 50),
shrinkage = c(0.01, 0.1),
n.minobsinnode = c(2, 20)),
verbose = FALSE,
trControl = control_object)set.seed(111)
nnet <- train(`Blowcount [Blows/m]` ~ `Normalised ENTRHU [-]` + `z [m]` + `skin friction API [kPa]`,
data = train,
method = "avNNet",
tuneGrid = expand.grid(decay = c(0.001, 0.01, 0.1),
size = seq(1, 27, by = 2),
bag = FALSE),
preProc = c("center", "scale"),
linout = TRUE,
trace = FALSE,
maxit = 1000,
trControl = control_object)set.seed(111)
rf <- train(`Blowcount [Blows/m]` ~ `Normalised ENTRHU [-]` + `z [m]` + `skin friction API [kPa]`,
data = train,
method = "rf",
tuneLength = 10,
ntrees = 1000,
importance = TRUE,
trControl = control_object)## note: only 2 unique complexity parameters in default grid. Truncating the grid to 2 .
set.seed(111)
cb <- train(`Blowcount [Blows/m]` ~ `Normalised ENTRHU [-]` + `z [m]` + `skin friction API [kPa]`,
data = train,
method = "cubist",
tuneGrid = expand.grid(.committees = c(1, 5, 10, 50, 75, 100),
.neighbors = c(0, 1, 3, 5, 7, 9)),
trControl = control_object)Stop parallel computing.
We put the models together and compare their performance in terms of RMSE.
all_models <- resamples(list("Boosted Trees" = gbm,
"Support Vector Machines" = svm,
"Neural Networks" = nnet,
"Random Forests" = rf,
"Cubist" = cb))
gbm_RMSE <- all_models$values %>%
select(Resample, `Boosted Trees~RMSE`) %>%
rename(RMSE = `Boosted Trees~RMSE`) %>%
mutate(Model = "Boosted Trees")
svm_RMSE <- all_models$values %>%
select(Resample, `Support Vector Machines~RMSE`) %>%
rename(RMSE = `Support Vector Machines~RMSE`) %>%
mutate(Model = "Support Vector Machines")
nnet_RMSE <- all_models$values %>%
select(Resample, `Neural Networks~RMSE`) %>%
rename(RMSE = `Neural Networks~RMSE`) %>%
mutate(Model = "Neural Networks")
rf_RMSE <- all_models$values %>%
select(Resample, `Random Forests~RMSE`) %>%
rename(RMSE = `Random Forests~RMSE`) %>%
mutate(Model = "Random Forests")
cb_RMSE <- all_models$values %>%
select(Resample, `Cubist~RMSE`) %>%
rename(RMSE = `Cubist~RMSE`) %>%
mutate(Model = "Cubist")
all_RMSE <- bind_rows(gbm_RMSE, svm_RMSE, nnet_RMSE, rf_RMSE, cb_RMSE)
kable(all_RMSE,
digits = 3,
caption = "RMSE values for all models.",
align = "r") %>%
kable_styling(full_width = T, font_size = 11, position = "center") %>%
row_spec(0, color = "grey10", background = "#F4F5F6") %>%
scroll_box(height = "300px")| Resample | RMSE | Model |
|---|---|---|
| Fold01.Rep1 | 16.099 | Boosted Trees |
| Fold01.Rep2 | 16.893 | Boosted Trees |
| Fold01.Rep3 | 16.585 | Boosted Trees |
| Fold01.Rep4 | 17.713 | Boosted Trees |
| Fold01.Rep5 | 16.167 | Boosted Trees |
| Fold02.Rep1 | 15.602 | Boosted Trees |
| Fold02.Rep2 | 14.386 | Boosted Trees |
| Fold02.Rep3 | 16.885 | Boosted Trees |
| Fold02.Rep4 | 16.177 | Boosted Trees |
| Fold02.Rep5 | 16.844 | Boosted Trees |
| Fold03.Rep1 | 15.678 | Boosted Trees |
| Fold03.Rep2 | 15.560 | Boosted Trees |
| Fold03.Rep3 | 16.715 | Boosted Trees |
| Fold03.Rep4 | 16.984 | Boosted Trees |
| Fold03.Rep5 | 16.601 | Boosted Trees |
| Fold04.Rep1 | 17.007 | Boosted Trees |
| Fold04.Rep2 | 16.288 | Boosted Trees |
| Fold04.Rep3 | 15.689 | Boosted Trees |
| Fold04.Rep4 | 16.703 | Boosted Trees |
| Fold04.Rep5 | 16.022 | Boosted Trees |
| Fold05.Rep1 | 15.156 | Boosted Trees |
| Fold05.Rep2 | 16.037 | Boosted Trees |
| Fold05.Rep3 | 13.842 | Boosted Trees |
| Fold05.Rep4 | 14.720 | Boosted Trees |
| Fold05.Rep5 | 15.199 | Boosted Trees |
| Fold06.Rep1 | 14.469 | Boosted Trees |
| Fold06.Rep2 | 16.216 | Boosted Trees |
| Fold06.Rep3 | 15.422 | Boosted Trees |
| Fold06.Rep4 | 15.421 | Boosted Trees |
| Fold06.Rep5 | 15.657 | Boosted Trees |
| Fold07.Rep1 | 17.102 | Boosted Trees |
| Fold07.Rep2 | 14.901 | Boosted Trees |
| Fold07.Rep3 | 16.116 | Boosted Trees |
| Fold07.Rep4 | 15.398 | Boosted Trees |
| Fold07.Rep5 | 15.292 | Boosted Trees |
| Fold08.Rep1 | 17.053 | Boosted Trees |
| Fold08.Rep2 | 18.169 | Boosted Trees |
| Fold08.Rep3 | 17.881 | Boosted Trees |
| Fold08.Rep4 | 15.278 | Boosted Trees |
| Fold08.Rep5 | 17.015 | Boosted Trees |
| Fold09.Rep1 | 15.866 | Boosted Trees |
| Fold09.Rep2 | 15.920 | Boosted Trees |
| Fold09.Rep3 | 15.409 | Boosted Trees |
| Fold09.Rep4 | 15.598 | Boosted Trees |
| Fold09.Rep5 | 15.215 | Boosted Trees |
| Fold10.Rep1 | 16.121 | Boosted Trees |
| Fold10.Rep2 | 15.896 | Boosted Trees |
| Fold10.Rep3 | 15.794 | Boosted Trees |
| Fold10.Rep4 | 16.319 | Boosted Trees |
| Fold10.Rep5 | 16.050 | Boosted Trees |
| Fold01.Rep1 | 16.528 | Support Vector Machines |
| Fold01.Rep2 | 17.512 | Support Vector Machines |
| Fold01.Rep3 | 17.377 | Support Vector Machines |
| Fold01.Rep4 | 17.636 | Support Vector Machines |
| Fold01.Rep5 | 16.696 | Support Vector Machines |
| Fold02.Rep1 | 15.211 | Support Vector Machines |
| Fold02.Rep2 | 14.638 | Support Vector Machines |
| Fold02.Rep3 | 17.494 | Support Vector Machines |
| Fold02.Rep4 | 16.876 | Support Vector Machines |
| Fold02.Rep5 | 17.053 | Support Vector Machines |
| Fold03.Rep1 | 16.236 | Support Vector Machines |
| Fold03.Rep2 | 15.947 | Support Vector Machines |
| Fold03.Rep3 | 16.766 | Support Vector Machines |
| Fold03.Rep4 | 17.163 | Support Vector Machines |
| Fold03.Rep5 | 16.893 | Support Vector Machines |
| Fold04.Rep1 | 17.762 | Support Vector Machines |
| Fold04.Rep2 | 16.280 | Support Vector Machines |
| Fold04.Rep3 | 15.758 | Support Vector Machines |
| Fold04.Rep4 | 17.225 | Support Vector Machines |
| Fold04.Rep5 | 16.387 | Support Vector Machines |
| Fold05.Rep1 | 15.095 | Support Vector Machines |
| Fold05.Rep2 | 16.318 | Support Vector Machines |
| Fold05.Rep3 | 14.439 | Support Vector Machines |
| Fold05.Rep4 | 15.100 | Support Vector Machines |
| Fold05.Rep5 | 15.536 | Support Vector Machines |
| Fold06.Rep1 | 14.680 | Support Vector Machines |
| Fold06.Rep2 | 16.166 | Support Vector Machines |
| Fold06.Rep3 | 15.621 | Support Vector Machines |
| Fold06.Rep4 | 15.561 | Support Vector Machines |
| Fold06.Rep5 | 16.468 | Support Vector Machines |
| Fold07.Rep1 | 17.722 | Support Vector Machines |
| Fold07.Rep2 | 15.570 | Support Vector Machines |
| Fold07.Rep3 | 16.511 | Support Vector Machines |
| Fold07.Rep4 | 15.512 | Support Vector Machines |
| Fold07.Rep5 | 15.492 | Support Vector Machines |
| Fold08.Rep1 | 17.612 | Support Vector Machines |
| Fold08.Rep2 | 18.556 | Support Vector Machines |
| Fold08.Rep3 | 18.004 | Support Vector Machines |
| Fold08.Rep4 | 15.315 | Support Vector Machines |
| Fold08.Rep5 | 17.318 | Support Vector Machines |
| Fold09.Rep1 | 16.165 | Support Vector Machines |
| Fold09.Rep2 | 15.728 | Support Vector Machines |
| Fold09.Rep3 | 15.348 | Support Vector Machines |
| Fold09.Rep4 | 15.760 | Support Vector Machines |
| Fold09.Rep5 | 14.962 | Support Vector Machines |
| Fold10.Rep1 | 16.286 | Support Vector Machines |
| Fold10.Rep2 | 16.657 | Support Vector Machines |
| Fold10.Rep3 | 16.292 | Support Vector Machines |
| Fold10.Rep4 | 17.151 | Support Vector Machines |
| Fold10.Rep5 | 16.732 | Support Vector Machines |
| Fold01.Rep1 | 15.712 | Neural Networks |
| Fold01.Rep2 | 17.280 | Neural Networks |
| Fold01.Rep3 | 16.732 | Neural Networks |
| Fold01.Rep4 | 17.049 | Neural Networks |
| Fold01.Rep5 | 16.238 | Neural Networks |
| Fold02.Rep1 | 15.229 | Neural Networks |
| Fold02.Rep2 | 14.674 | Neural Networks |
| Fold02.Rep3 | 16.739 | Neural Networks |
| Fold02.Rep4 | 16.288 | Neural Networks |
| Fold02.Rep5 | 17.125 | Neural Networks |
| Fold03.Rep1 | 15.698 | Neural Networks |
| Fold03.Rep2 | 16.077 | Neural Networks |
| Fold03.Rep3 | 16.385 | Neural Networks |
| Fold03.Rep4 | 16.694 | Neural Networks |
| Fold03.Rep5 | 16.120 | Neural Networks |
| Fold04.Rep1 | 17.438 | Neural Networks |
| Fold04.Rep2 | 16.053 | Neural Networks |
| Fold04.Rep3 | 16.010 | Neural Networks |
| Fold04.Rep4 | 16.846 | Neural Networks |
| Fold04.Rep5 | 15.529 | Neural Networks |
| Fold05.Rep1 | 15.212 | Neural Networks |
| Fold05.Rep2 | 16.373 | Neural Networks |
| Fold05.Rep3 | 14.265 | Neural Networks |
| Fold05.Rep4 | 14.928 | Neural Networks |
| Fold05.Rep5 | 14.990 | Neural Networks |
| Fold06.Rep1 | 14.231 | Neural Networks |
| Fold06.Rep2 | 15.652 | Neural Networks |
| Fold06.Rep3 | 15.506 | Neural Networks |
| Fold06.Rep4 | 15.845 | Neural Networks |
| Fold06.Rep5 | 16.084 | Neural Networks |
| Fold07.Rep1 | 16.947 | Neural Networks |
| Fold07.Rep2 | 15.005 | Neural Networks |
| Fold07.Rep3 | 16.106 | Neural Networks |
| Fold07.Rep4 | 15.244 | Neural Networks |
| Fold07.Rep5 | 15.837 | Neural Networks |
| Fold08.Rep1 | 17.052 | Neural Networks |
| Fold08.Rep2 | 17.792 | Neural Networks |
| Fold08.Rep3 | 17.731 | Neural Networks |
| Fold08.Rep4 | 15.392 | Neural Networks |
| Fold08.Rep5 | 16.677 | Neural Networks |
| Fold09.Rep1 | 15.714 | Neural Networks |
| Fold09.Rep2 | 15.365 | Neural Networks |
| Fold09.Rep3 | 14.935 | Neural Networks |
| Fold09.Rep4 | 16.019 | Neural Networks |
| Fold09.Rep5 | 15.497 | Neural Networks |
| Fold10.Rep1 | 16.431 | Neural Networks |
| Fold10.Rep2 | 16.211 | Neural Networks |
| Fold10.Rep3 | 15.924 | Neural Networks |
| Fold10.Rep4 | 16.659 | Neural Networks |
| Fold10.Rep5 | 16.499 | Neural Networks |
| Fold01.Rep1 | 15.365 | Random Forests |
| Fold01.Rep2 | 16.292 | Random Forests |
| Fold01.Rep3 | 15.618 | Random Forests |
| Fold01.Rep4 | 16.994 | Random Forests |
| Fold01.Rep5 | 15.255 | Random Forests |
| Fold02.Rep1 | 15.714 | Random Forests |
| Fold02.Rep2 | 14.688 | Random Forests |
| Fold02.Rep3 | 16.411 | Random Forests |
| Fold02.Rep4 | 15.962 | Random Forests |
| Fold02.Rep5 | 16.036 | Random Forests |
| Fold03.Rep1 | 15.311 | Random Forests |
| Fold03.Rep2 | 14.772 | Random Forests |
| Fold03.Rep3 | 16.543 | Random Forests |
| Fold03.Rep4 | 16.033 | Random Forests |
| Fold03.Rep5 | 15.624 | Random Forests |
| Fold04.Rep1 | 16.148 | Random Forests |
| Fold04.Rep2 | 16.332 | Random Forests |
| Fold04.Rep3 | 14.804 | Random Forests |
| Fold04.Rep4 | 16.231 | Random Forests |
| Fold04.Rep5 | 15.388 | Random Forests |
| Fold05.Rep1 | 14.062 | Random Forests |
| Fold05.Rep2 | 15.585 | Random Forests |
| Fold05.Rep3 | 13.019 | Random Forests |
| Fold05.Rep4 | 13.993 | Random Forests |
| Fold05.Rep5 | 14.575 | Random Forests |
| Fold06.Rep1 | 13.792 | Random Forests |
| Fold06.Rep2 | 16.092 | Random Forests |
| Fold06.Rep3 | 14.980 | Random Forests |
| Fold06.Rep4 | 14.497 | Random Forests |
| Fold06.Rep5 | 14.712 | Random Forests |
| Fold07.Rep1 | 16.143 | Random Forests |
| Fold07.Rep2 | 14.246 | Random Forests |
| Fold07.Rep3 | 15.592 | Random Forests |
| Fold07.Rep4 | 14.521 | Random Forests |
| Fold07.Rep5 | 15.323 | Random Forests |
| Fold08.Rep1 | 16.371 | Random Forests |
| Fold08.Rep2 | 16.933 | Random Forests |
| Fold08.Rep3 | 17.364 | Random Forests |
| Fold08.Rep4 | 14.776 | Random Forests |
| Fold08.Rep5 | 16.323 | Random Forests |
| Fold09.Rep1 | 14.631 | Random Forests |
| Fold09.Rep2 | 15.350 | Random Forests |
| Fold09.Rep3 | 14.735 | Random Forests |
| Fold09.Rep4 | 15.857 | Random Forests |
| Fold09.Rep5 | 14.345 | Random Forests |
| Fold10.Rep1 | 15.068 | Random Forests |
| Fold10.Rep2 | 14.500 | Random Forests |
| Fold10.Rep3 | 15.365 | Random Forests |
| Fold10.Rep4 | 15.519 | Random Forests |
| Fold10.Rep5 | 15.448 | Random Forests |
| Fold01.Rep1 | 15.883 | Cubist |
| Fold01.Rep2 | 16.848 | Cubist |
| Fold01.Rep3 | 16.438 | Cubist |
| Fold01.Rep4 | 17.456 | Cubist |
| Fold01.Rep5 | 16.010 | Cubist |
| Fold02.Rep1 | 15.574 | Cubist |
| Fold02.Rep2 | 14.889 | Cubist |
| Fold02.Rep3 | 16.890 | Cubist |
| Fold02.Rep4 | 16.065 | Cubist |
| Fold02.Rep5 | 17.243 | Cubist |
| Fold03.Rep1 | 15.856 | Cubist |
| Fold03.Rep2 | 15.958 | Cubist |
| Fold03.Rep3 | 16.510 | Cubist |
| Fold03.Rep4 | 16.660 | Cubist |
| Fold03.Rep5 | 15.962 | Cubist |
| Fold04.Rep1 | 17.266 | Cubist |
| Fold04.Rep2 | 16.067 | Cubist |
| Fold04.Rep3 | 15.655 | Cubist |
| Fold04.Rep4 | 16.260 | Cubist |
| Fold04.Rep5 | 15.647 | Cubist |
| Fold05.Rep1 | 14.726 | Cubist |
| Fold05.Rep2 | 16.052 | Cubist |
| Fold05.Rep3 | 13.820 | Cubist |
| Fold05.Rep4 | 14.646 | Cubist |
| Fold05.Rep5 | 15.088 | Cubist |
| Fold06.Rep1 | 14.632 | Cubist |
| Fold06.Rep2 | 15.992 | Cubist |
| Fold06.Rep3 | 15.044 | Cubist |
| Fold06.Rep4 | 15.200 | Cubist |
| Fold06.Rep5 | 15.592 | Cubist |
| Fold07.Rep1 | 16.871 | Cubist |
| Fold07.Rep2 | 14.986 | Cubist |
| Fold07.Rep3 | 16.169 | Cubist |
| Fold07.Rep4 | 15.456 | Cubist |
| Fold07.Rep5 | 15.695 | Cubist |
| Fold08.Rep1 | 17.076 | Cubist |
| Fold08.Rep2 | 17.512 | Cubist |
| Fold08.Rep3 | 17.701 | Cubist |
| Fold08.Rep4 | 15.363 | Cubist |
| Fold08.Rep5 | 17.108 | Cubist |
| Fold09.Rep1 | 15.283 | Cubist |
| Fold09.Rep2 | 15.736 | Cubist |
| Fold09.Rep3 | 15.292 | Cubist |
| Fold09.Rep4 | 15.804 | Cubist |
| Fold09.Rep5 | 14.847 | Cubist |
| Fold10.Rep1 | 15.642 | Cubist |
| Fold10.Rep2 | 15.590 | Cubist |
| Fold10.Rep3 | 16.190 | Cubist |
| Fold10.Rep4 | 16.659 | Cubist |
| Fold10.Rep5 | 15.971 | Cubist |
The above table contains the RMSE values for the resampling results for each model. A plot of these data is shown in the following.
median_RMSE <- all_RMSE %>%
group_by(Model) %>%
summarise(`median RMSE` = median(RMSE))
all_RMSE %>%
ggplot(aes(RMSE, fct_reorder(Model, RMSE, median))) +
geom_point(aes(fill = RMSE), size = 5, shape = 21, colour = "grey") +
scale_fill_distiller(palette = "Spectral") +
labs(y = "") +
theme(panel.grid.minor = element_blank(),
legend.position = "none") +
geom_point(data = median_RMSE, aes(`median RMSE`, Model), size = 1, colour = "grey20", shape = "|", stroke = 10)
RMSE values for the resampling results for each model
test datasetWe validate the best model (Random Forests) by checking it’s performance against the test dataset.
test$predictions <- predict(rf, newdata = test)
sqrt(sum((test$'Blowcount [Blows/m]' - test$predictions)^2)/nrow(test))## [1] 13.57531
plot_rf1 <- test %>%
ggplot(aes(`Normalised ENTRHU [-]`, `Blowcount [Blows/m]`)) +
geom_point(alpha = 1/20) +
geom_point(data = test, aes(`Normalised ENTRHU [-]`, predictions), color = "steelblue", alpha = 1) +
expand_limits(x = 0, y = 0) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Model fit vs actual data.",
x = "Normalised ENTRHU [-]",
y = "Blowcount [Blows/m]")
plot_rf2 <- test %>%
ggplot(aes(predictions, `Blowcount [Blows/m]`)) +
geom_point(alpha = 1/20) +
expand_limits(x = 0, y = 0) +
geom_abline(color = "grey20", size = 1) +
coord_fixed(xlim = c(0, 150), ylim = c(0, 150)) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Prediction vs actual Blowcount [Blows/m]",
x = "Predicted Blowcount [Blows/m]",
y = "Actual Blowcount [Blows/m]")
plot_rf3 <- test %>%
ggplot(aes(`Normalised ENTRHU [-]`, predictions - `Blowcount [Blows/m]`)) +
geom_point(alpha = 1/20) +
geom_hline(aes(yintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Scatter plot of residuals.",
x = "Normalised ENTRHU [-]",
y = "Residuals")
plot_rf4 <- test %>%
ggplot(aes(predictions - `Blowcount [Blows/m]`)) +
geom_histogram(binwidth = 2, alpha = 1/5) +
geom_vline(aes(xintercept = 0), color = "grey20", size = 1) +
theme(panel.grid.minor = element_blank(),
aspect.ratio = 1,
plot.title = element_text(size = 9, face = "italic")) +
labs(title = "Histogram of residuals.",
x = "Residuals",
y = "Count")
patch_rf1 <- plot_rf1 + plot_rf2
patch_rf2 <- plot_rf3 + plot_rf4
patch_rf1 / patch_rf2
Results for Random Forest model applied to the test dataset
test %>%
group_by(`Location ID`) %>%
summarise(actual_blows = sum(`Blowcount [Blows/m]`),
predicted_blows = sum(predictions)) %>%
ggplot(aes(predicted_blows, actual_blows)) +
geom_point(alpha = 1, size = 5, aes(colour = abs(predicted_blows - actual_blows)/actual_blows)) +
scale_colour_distiller(palette = "Spectral", limits = c(0, 0.4), labels = percent) +
coord_fixed(xlim = c(1800, 4000), ylim = c(1800, 4000)) +
theme(panel.grid.minor = element_blank(),
legend.key.height = unit(2.25, "cm")) +
labs(x = "Predicted number of blows",
y = "Actual number of blows",
colour = "Error") +
geom_abline(color = "grey20", size = 1)
Predicted vs actual number of total blows for test locations.
test %>%
group_by(`Location ID`) %>%
summarise(actual_blows = sum(`Blowcount [Blows/m]`),
predicted_blows = sum(predictions)) %>%
summarise(median_error = median(abs(predicted_blows - actual_blows)/actual_blows))## # A tibble: 1 x 1
## median_error
## <dbl>
## 1 0.0690
Further improvement: etc